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CRAN checks API

CRAN checks API is an API providing access to CRAN checks data in a machine-readable format.

For a recent overview of the API, check out the Overview of the CRAN checks API blog post on the R-Hub blog.

The API was created and is maintained by Scott Chamberlain.

Base URL

https://cranchecks.info/

R package cchecks πŸ“¦

Most endpoints are supported in the cchecks R client.

remotes::install_github("ropenscilabs/cchecks")
library("cchecks")

Authentication

Endpoints without authentication

docs/, heartbeat/, maintainers/, packages/, history/, badges/, search/

Endpoints with authentication

notifications/

HTTP methods

This is mostly a read only API. That is, we only allow GET (and HEAD) requests on this API.

Requests of all other types will be rejected with appropriate 405 code.

Media types

JSON mostly, image/svg+xml for badges.

Pagination

The query parameters limit (default = 10) and offset (default = 0) can be sent.

limit (integer, optional)
number of records to return. Default: 10. Max: see endpoint docs.
offset (integer, optional)
Record number to start at. Default: 0. Max: see endpoint docs.

Above parameters can be used only on /pkgs, /maintainers, and /pkgs/{package_name}/history.

The response body from the server will include data on records found in found and number returned in count.

Response codes

For this section of the docs, examples of (bad) requests are given in the Shell tab on the right, and the headers are in the Headers tab.

200 (OK)

Request good!

curl https://cranchecks.info/heartbeat | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 256
date: Mon, 18 Jan 2021 13:34:40 GMT

{
    "routes": [
        "/",
        "/docs",
        "/heartbeat/?",
        "/pkgs",
        "/pkgs/:name",
        "/maintainers",
        "/maintainers/:email",
        "/badges/:type/:package",
        "/badges/flavor/:flavor/:package",
        "/pkgs/:name/history",
        "/history/:date",
        "/search",
        "/notifications/rules",
        "/notifications/rules/:id"
    ]
}

204 (No Content)

Given on a delete request, indicating success, response body should be empty.

curl -XDELETE -H "Authorization: Bearer ***" \
 https://cranchecks.info/notifications/rules/484
HTTP/2 204 
access-control-allow-methods: DELETE
server: Caddy
x-content-type-options: nosniff
date: Mon, 18 Jan 2021 13:34:40 GMT

302 (Found)

The root /, redirects to /heartbeat, and /docs redirects to what you’re reading right now.

curl https://cranchecks.info/ | jq .
HTTP/2 302 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
location: https://cranchecks.info/heartbeat
server: Caddy
x-content-type-options: nosniff
content-length: 0
date: Mon, 18 Jan 2021 13:34:40 GMT

400 (Bad request)

When you have a malformed request, fix it and try again

curl https://cranchecks.info/maintainers/blablabla | jq .
HTTP/2 400 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 52
date: Mon, 18 Jan 2021 13:34:41 GMT

{
    "error": {
        "message": "no results found"
    },
    "data": null
}

404 (Not found)

When you request a route that does not exist, fix it and try again. E.g. in the example here we misspell “maintainers” to “maintainer”. πŸ™ˆ

curl https://cranchecks.info/maintainer | jq .
HTTP/2 404 
content-type: application/json
server: Caddy
x-cascade: pass
x-content-type-options: nosniff
content-length: 27
date: Mon, 18 Jan 2021 13:34:41 GMT

{
    "error": "route not found"
}

405 (Method not allowed)

When you use a prohibited HTTP method for a given route. Don’t do that. πŸ˜‰

curl -XDELETE https://cranchecks.info/pkgs/ropenaq/
HTTP/2 405 
content-type: application/json
server: Caddy
x-content-type-options: nosniff
content-length: 30
date: Mon, 18 Jan 2021 13:34:41 GMT

{
    "error": "Method Not Allowed"
}

500 (Internal server error)

Server got itself in trouble 😱 😭; get in touch with us.

HTTP/2 500
date: Thu, 21 May 2020 16:53:33 GMT
content-type: text/html; charset=utf-8
content-length: 0

Root

This path redirects to /heartbeat

curl https://cranchecks.info/ | jq .
HTTP/2 302 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
location: https://cranchecks.info/heartbeat
server: Caddy
x-content-type-options: nosniff
content-length: 0
date: Mon, 18 Jan 2021 13:34:42 GMT

Heartbeat

Get heartbeat for the API πŸ’“ (i.e. the list of endpoints).

GET [/heartbeat]

Function of the cchecks R package: cch_heartbeat().

curl https://cranchecks.info/heartbeat | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 256
date: Mon, 18 Jan 2021 13:34:24 GMT

{
    "routes": [
        "/",
        "/docs",
        "/heartbeat/?",
        "/pkgs",
        "/pkgs/:name",
        "/maintainers",
        "/maintainers/:email",
        "/badges/:type/:package",
        "/badges/flavor/:flavor/:package",
        "/pkgs/:name/history",
        "/history/:date",
        "/search",
        "/notifications/rules",
        "/notifications/rules/:id"
    ]
}
cchecks::cch_heartbeat()
$routes
 [1] "/"                               "/docs"                          
 [3] "/heartbeat/?"                    "/pkgs"                          
 [5] "/pkgs/:name"                     "/maintainers"                   
 [7] "/maintainers/:email"             "/badges/:type/:package"         
 [9] "/badges/flavor/:flavor/:package" "/pkgs/:name/history"            
[11] "/history/:date"                  "/search"                        
[13] "/notifications/rules"            "/notifications/rules/:id"       

Docs

This path redirects to the docs (hopefully here! 😸). See Headers tab.

GET [/docs]

curl https://cranchecks.info/docs
HTTP/2 301 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
location: https://docs.cranchecks.info/
server: Caddy
x-content-type-options: nosniff
content-length: 0
date: Mon, 18 Jan 2021 13:34:24 GMT

Maintainers

Get checks data by package maintainer.

Function of the cchecks R package: cch_maintainers().

Maintainers

Get checks summaries for all maintainers.

GET [/maintainers]

Default limit of 10, max of 1000.

Example with custom offset and limit.

curl https://cranchecks.info/maintainers/?limit=2&offset=5 | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 1060
date: Mon, 18 Jan 2021 13:34:27 GMT

{
    "found": 10439,
    "count": 2,
    "offset": 5,
    "error": null,
    "data": [
        {
            "email": "markus.boenn.sf_at_googlemail.com",
            "name": "MarkusΒ Boenn",
            "url": "https://cloud.r-project.org/web/checks/check_results_markus.boenn.sf_at_googlemail.com.html",
            "date_updated": "2021-01-18T12:03:01.090Z",
            "table": [
                {
                    "package": "hypergea",
                    "any": true,
                    "ok": 6,
                    "note": 6,
                    "warn": 0,
                    "error": 0
                }
            ],
            "packages": [
                {
                    "package": "hypergea",
                    "url": "https://cloud.r-project.org/web/checks/check_results_hypergea.html",
                    "check_result": [
                        {
                            "category": "NOTE",
                            "number_checks": 6
                        },
                        {
                            "category": "OK",
                            "number_checks": 6
                        }
                    ],
                    "version": null
                }
            ]
        },
        {
            "email": "s.greilich_at_dkfz.de",
            "name": "SteffenΒ Greilich",
            "url": "https://cloud.r-project.org/web/checks/check_results_s.greilich_at_dkfz.de.html",
            "date_updated": "2021-01-18T12:03:01.090Z",
            "table": [
                {
                    "package": "libamtrack",
                    "any": true,
                    "ok": 2,
                    "note": 10,
                    "warn": 0,
                    "error": 0
                }
            ],
            "packages": [
                {
                    "package": "libamtrack",
                    "url": "https://cloud.r-project.org/web/checks/check_results_libamtrack.html",
                    "check_result": [
                        {
                            "category": "NOTE",
                            "number_checks": 10
                        },
                        {
                            "category": "OK",
                            "number_checks": 2
                        }
                    ],
                    "version": null
                }
            ]
        }
    ]
}
cchecks::cch_maintainers(limit = 2, offset = 5)
$found
[1] 10439

$count
[1] 2

$offset
[1] 5

$error
NULL

$data
                              email             name
1 markus.boenn.sf_at_googlemail.com     MarkusΒ Boenn
2             s.greilich_at_dkfz.de SteffenΒ Greilich
                                                                                          url
1 https://cloud.r-project.org/web/checks/check_results_markus.boenn.sf_at_googlemail.com.html
2             https://cloud.r-project.org/web/checks/check_results_s.greilich_at_dkfz.de.html
              date_updated                         table
1 2021-01-18T12:03:01.090Z    hypergea, TRUE, 6, 6, 0, 0
2 2021-01-18T12:03:01.090Z libamtrack, TRUE, 2, 10, 0, 0
                                                                                               packages
1      hypergea, https://cloud.r-project.org/web/checks/check_results_hypergea.html, NOTE, OK, 6, 6, NA
2 libamtrack, https://cloud.r-project.org/web/checks/check_results_libamtrack.html, NOTE, OK, 10, 2, NA

Maintainers by email

Get checks data by email of the package maintainer.

GET [/maintainers/{email}]

⚠️ jane.doe@example.com has to be transformed to jane.doe_at_example.com.

curl https://cranchecks.info/maintainers/csardi.gabor_at_gmail.com | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 13131
date: Mon, 18 Jan 2021 13:34:28 GMT

{
    "error": null,
    "data": {
        "email": "csardi.gabor_at_gmail.com",
        "name": "GΓ‘borΒ CsΓ‘rdi",
        "url": "https://cloud.r-project.org/web/checks/check_results_csardi.gabor_at_gmail.com.html",
        "date_updated": "2021-01-18T12:03:01.616Z",
        "table": [
            {
                "package": "asciicast",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "callr",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "cleancall",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "cli",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "cliapp",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "clisymbols",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "cranlike",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "cranlogs",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "crayon",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "debugme",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "desc",
                "any": true,
                "ok": 11,
                "note": 0,
                "warn": 0,
                "error": 1
            },
            {
                "package": "disposables",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "dotenv",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "filelock",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "gh",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "gitcreds",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "igraph",
                "any": true,
                "ok": 5,
                "note": 7,
                "warn": 0,
                "error": 0
            },
            {
                "package": "igraphdata",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "isa2",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "keypress",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "keyring",
                "any": true,
                "ok": 10,
                "note": 2,
                "warn": 0,
                "error": 0
            },
            {
                "package": "liteq",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "lpSolve",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "oskeyring",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "pak",
                "any": true,
                "ok": 11,
                "note": 1,
                "warn": 0,
                "error": 0
            },
            {
                "package": "parsedate",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "pingr",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "pkgcache",
                "any": true,
                "ok": 11,
                "note": 0,
                "warn": 0,
                "error": 1
            },
            {
                "package": "pkgconfig",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "pkgsearch",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "praise",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "presser",
                "any": true,
                "ok": 11,
                "note": 0,
                "warn": 0,
                "error": 1
            },
            {
                "package": "prettycode",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "prettyunits",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "processx",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "progress",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "ps",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "rcmdcheck",
                "any": true,
                "ok": 11,
                "note": 1,
                "warn": 0,
                "error": 0
            },
            {
                "package": "rcorpora",
                "any": true,
                "ok": 5,
                "note": 7,
                "warn": 0,
                "error": 0
            },
            {
                "package": "rematch2",
                "any": true,
                "ok": 11,
                "note": 1,
                "warn": 0,
                "error": 0
            },
            {
                "package": "rhub",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "rversions",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "sankey",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "secret",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "sessioninfo",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "showimage",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "tracer",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "webdriver",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "webfakes",
                "any": true,
                "ok": 11,
                "note": 0,
                "warn": 0,
                "error": 1
            },
            {
                "package": "whoami",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "xmlparsedata",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "xopen",
                "any": false,
                "ok": 12,
                "note": 0,
                "warn": 0,
                "error": 0
            },
            {
                "package": "zip",
                "any": true,
                "ok": 11,
                "note": 0,
                "warn": 0,
                "error": 1
            }
        ],
        "packages": [
            {
                "package": "asciicast",
                "url": "https://cloud.r-project.org/web/checks/check_results_asciicast.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "callr",
                "url": "https://cloud.r-project.org/web/checks/check_results_callr.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "cleancall",
                "url": "https://cloud.r-project.org/web/checks/check_results_cleancall.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "cli",
                "url": "https://cloud.r-project.org/web/checks/check_results_cli.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "cliapp",
                "url": "https://cloud.r-project.org/web/checks/check_results_cliapp.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "clisymbols",
                "url": "https://cloud.r-project.org/web/checks/check_results_clisymbols.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "cranlike",
                "url": "https://cloud.r-project.org/web/checks/check_results_cranlike.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "cranlogs",
                "url": "https://cloud.r-project.org/web/checks/check_results_cranlogs.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "crayon",
                "url": "https://cloud.r-project.org/web/checks/check_results_crayon.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "debugme",
                "url": "https://cloud.r-project.org/web/checks/check_results_debugme.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "desc",
                "url": "https://cloud.r-project.org/web/checks/check_results_desc.html",
                "check_result": [
                    {
                        "category": "ERROR",
                        "number_checks": 1
                    },
                    {
                        "category": "OK",
                        "number_checks": 11
                    }
                ],
                "version": null
            },
            {
                "package": "disposables",
                "url": "https://cloud.r-project.org/web/checks/check_results_disposables.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "dotenv",
                "url": "https://cloud.r-project.org/web/checks/check_results_dotenv.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "filelock",
                "url": "https://cloud.r-project.org/web/checks/check_results_filelock.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "gh",
                "url": "https://cloud.r-project.org/web/checks/check_results_gh.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "gitcreds",
                "url": "https://cloud.r-project.org/web/checks/check_results_gitcreds.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "igraph",
                "url": "https://cloud.r-project.org/web/checks/check_results_igraph.html",
                "check_result": [
                    {
                        "category": "NOTE",
                        "number_checks": 7
                    },
                    {
                        "category": "OK",
                        "number_checks": 5
                    }
                ],
                "version": null
            },
            {
                "package": "igraphdata",
                "url": "https://cloud.r-project.org/web/checks/check_results_igraphdata.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "isa2",
                "url": "https://cloud.r-project.org/web/checks/check_results_isa2.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "keypress",
                "url": "https://cloud.r-project.org/web/checks/check_results_keypress.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "keyring",
                "url": "https://cloud.r-project.org/web/checks/check_results_keyring.html",
                "check_result": [
                    {
                        "category": "NOTE",
                        "number_checks": 2
                    },
                    {
                        "category": "OK",
                        "number_checks": 10
                    }
                ],
                "version": null
            },
            {
                "package": "liteq",
                "url": "https://cloud.r-project.org/web/checks/check_results_liteq.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "lpSolve",
                "url": "https://cloud.r-project.org/web/checks/check_results_lpSolve.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "oskeyring",
                "url": "https://cloud.r-project.org/web/checks/check_results_oskeyring.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "pak",
                "url": "https://cloud.r-project.org/web/checks/check_results_pak.html",
                "check_result": [
                    {
                        "category": "NOTE",
                        "number_checks": 1
                    },
                    {
                        "category": "OK",
                        "number_checks": 11
                    }
                ],
                "version": null
            },
            {
                "package": "parsedate",
                "url": "https://cloud.r-project.org/web/checks/check_results_parsedate.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "pingr",
                "url": "https://cloud.r-project.org/web/checks/check_results_pingr.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "pkgcache",
                "url": "https://cloud.r-project.org/web/checks/check_results_pkgcache.html",
                "check_result": [
                    {
                        "category": "ERROR",
                        "number_checks": 1
                    },
                    {
                        "category": "OK",
                        "number_checks": 11
                    }
                ],
                "version": null
            },
            {
                "package": "pkgconfig",
                "url": "https://cloud.r-project.org/web/checks/check_results_pkgconfig.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "pkgsearch",
                "url": "https://cloud.r-project.org/web/checks/check_results_pkgsearch.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "praise",
                "url": "https://cloud.r-project.org/web/checks/check_results_praise.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "presser",
                "url": "https://cloud.r-project.org/web/checks/check_results_presser.html",
                "check_result": [
                    {
                        "category": "ERROR",
                        "number_checks": 1
                    },
                    {
                        "category": "OK",
                        "number_checks": 11
                    }
                ],
                "version": null
            },
            {
                "package": "prettycode",
                "url": "https://cloud.r-project.org/web/checks/check_results_prettycode.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "prettyunits",
                "url": "https://cloud.r-project.org/web/checks/check_results_prettyunits.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "processx",
                "url": "https://cloud.r-project.org/web/checks/check_results_processx.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "progress",
                "url": "https://cloud.r-project.org/web/checks/check_results_progress.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "ps",
                "url": "https://cloud.r-project.org/web/checks/check_results_ps.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "rcmdcheck",
                "url": "https://cloud.r-project.org/web/checks/check_results_rcmdcheck.html",
                "check_result": [
                    {
                        "category": "NOTE",
                        "number_checks": 1
                    },
                    {
                        "category": "OK",
                        "number_checks": 11
                    }
                ],
                "version": null
            },
            {
                "package": "rcorpora",
                "url": "https://cloud.r-project.org/web/checks/check_results_rcorpora.html",
                "check_result": [
                    {
                        "category": "NOTE",
                        "number_checks": 7
                    },
                    {
                        "category": "OK",
                        "number_checks": 5
                    }
                ],
                "version": null
            },
            {
                "package": "rematch2",
                "url": "https://cloud.r-project.org/web/checks/check_results_rematch2.html",
                "check_result": [
                    {
                        "category": "NOTE",
                        "number_checks": 1
                    },
                    {
                        "category": "OK",
                        "number_checks": 11
                    }
                ],
                "version": null
            },
            {
                "package": "rhub",
                "url": "https://cloud.r-project.org/web/checks/check_results_rhub.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "rversions",
                "url": "https://cloud.r-project.org/web/checks/check_results_rversions.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "sankey",
                "url": "https://cloud.r-project.org/web/checks/check_results_sankey.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "secret",
                "url": "https://cloud.r-project.org/web/checks/check_results_secret.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "sessioninfo",
                "url": "https://cloud.r-project.org/web/checks/check_results_sessioninfo.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "showimage",
                "url": "https://cloud.r-project.org/web/checks/check_results_showimage.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "tracer",
                "url": "https://cloud.r-project.org/web/checks/check_results_tracer.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "webdriver",
                "url": "https://cloud.r-project.org/web/checks/check_results_webdriver.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "webfakes",
                "url": "https://cloud.r-project.org/web/checks/check_results_webfakes.html",
                "check_result": [
                    {
                        "category": "ERROR",
                        "number_checks": 1
                    },
                    {
                        "category": "OK",
                        "number_checks": 11
                    }
                ],
                "version": null
            },
            {
                "package": "whoami",
                "url": "https://cloud.r-project.org/web/checks/check_results_whoami.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "xmlparsedata",
                "url": "https://cloud.r-project.org/web/checks/check_results_xmlparsedata.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "xopen",
                "url": "https://cloud.r-project.org/web/checks/check_results_xopen.html",
                "check_result": [
                    {
                        "category": "OK",
                        "number_checks": 12
                    }
                ],
                "version": null
            },
            {
                "package": "zip",
                "url": "https://cloud.r-project.org/web/checks/check_results_zip.html",
                "check_result": [
                    {
                        "category": "ERROR",
                        "number_checks": 1
                    },
                    {
                        "category": "OK",
                        "number_checks": 11
                    }
                ],
                "version": null
            }
        ]
    }
}
cchecks::cch_maintainers(x = "csardi.gabor_at_gmail.com")
$error
NULL

$data
$data$email
[1] "csardi.gabor_at_gmail.com"

$data$name
[1] "GΓ‘borΒ CsΓ‘rdi"

$data$url
[1] "https://cloud.r-project.org/web/checks/check_results_csardi.gabor_at_gmail.com.html"

$data$date_updated
[1] "2021-01-18T12:03:01.616Z"

$data$table
        package   any ok note warn error
1     asciicast FALSE 12    0    0     0
2         callr FALSE 12    0    0     0
3     cleancall FALSE 12    0    0     0
4           cli FALSE 12    0    0     0
5        cliapp FALSE 12    0    0     0
6    clisymbols FALSE 12    0    0     0
7      cranlike FALSE 12    0    0     0
8      cranlogs FALSE 12    0    0     0
9        crayon FALSE 12    0    0     0
10      debugme FALSE 12    0    0     0
11         desc  TRUE 11    0    0     1
12  disposables FALSE 12    0    0     0
13       dotenv FALSE 12    0    0     0
14     filelock FALSE 12    0    0     0
15           gh FALSE 12    0    0     0
16     gitcreds FALSE 12    0    0     0
17       igraph  TRUE  5    7    0     0
18   igraphdata FALSE 12    0    0     0
19         isa2 FALSE 12    0    0     0
20     keypress FALSE 12    0    0     0
21      keyring  TRUE 10    2    0     0
22        liteq FALSE 12    0    0     0
23      lpSolve FALSE 12    0    0     0
24    oskeyring FALSE 12    0    0     0
25          pak  TRUE 11    1    0     0
26    parsedate FALSE 12    0    0     0
27        pingr FALSE 12    0    0     0
28     pkgcache  TRUE 11    0    0     1
29    pkgconfig FALSE 12    0    0     0
30    pkgsearch FALSE 12    0    0     0
31       praise FALSE 12    0    0     0
32      presser  TRUE 11    0    0     1
33   prettycode FALSE 12    0    0     0
34  prettyunits FALSE 12    0    0     0
35     processx FALSE 12    0    0     0
36     progress FALSE 12    0    0     0
37           ps FALSE 12    0    0     0
38    rcmdcheck  TRUE 11    1    0     0
39     rcorpora  TRUE  5    7    0     0
40     rematch2  TRUE 11    1    0     0
41         rhub FALSE 12    0    0     0
42    rversions FALSE 12    0    0     0
43       sankey FALSE 12    0    0     0
44       secret FALSE 12    0    0     0
45  sessioninfo FALSE 12    0    0     0
46    showimage FALSE 12    0    0     0
47       tracer FALSE 12    0    0     0
48    webdriver FALSE 12    0    0     0
49     webfakes  TRUE 11    0    0     1
50       whoami FALSE 12    0    0     0
51 xmlparsedata FALSE 12    0    0     0
52        xopen FALSE 12    0    0     0
53          zip  TRUE 11    0    0     1

$data$packages
        package
1     asciicast
2         callr
3     cleancall
4           cli
5        cliapp
6    clisymbols
7      cranlike
8      cranlogs
9        crayon
10      debugme
11         desc
12  disposables
13       dotenv
14     filelock
15           gh
16     gitcreds
17       igraph
18   igraphdata
19         isa2
20     keypress
21      keyring
22        liteq
23      lpSolve
24    oskeyring
25          pak
26    parsedate
27        pingr
28     pkgcache
29    pkgconfig
30    pkgsearch
31       praise
32      presser
33   prettycode
34  prettyunits
35     processx
36     progress
37           ps
38    rcmdcheck
39     rcorpora
40     rematch2
41         rhub
42    rversions
43       sankey
44       secret
45  sessioninfo
46    showimage
47       tracer
48    webdriver
49     webfakes
50       whoami
51 xmlparsedata
52        xopen
53          zip
                                                                      url
1     https://cloud.r-project.org/web/checks/check_results_asciicast.html
2         https://cloud.r-project.org/web/checks/check_results_callr.html
3     https://cloud.r-project.org/web/checks/check_results_cleancall.html
4           https://cloud.r-project.org/web/checks/check_results_cli.html
5        https://cloud.r-project.org/web/checks/check_results_cliapp.html
6    https://cloud.r-project.org/web/checks/check_results_clisymbols.html
7      https://cloud.r-project.org/web/checks/check_results_cranlike.html
8      https://cloud.r-project.org/web/checks/check_results_cranlogs.html
9        https://cloud.r-project.org/web/checks/check_results_crayon.html
10      https://cloud.r-project.org/web/checks/check_results_debugme.html
11         https://cloud.r-project.org/web/checks/check_results_desc.html
12  https://cloud.r-project.org/web/checks/check_results_disposables.html
13       https://cloud.r-project.org/web/checks/check_results_dotenv.html
14     https://cloud.r-project.org/web/checks/check_results_filelock.html
15           https://cloud.r-project.org/web/checks/check_results_gh.html
16     https://cloud.r-project.org/web/checks/check_results_gitcreds.html
17       https://cloud.r-project.org/web/checks/check_results_igraph.html
18   https://cloud.r-project.org/web/checks/check_results_igraphdata.html
19         https://cloud.r-project.org/web/checks/check_results_isa2.html
20     https://cloud.r-project.org/web/checks/check_results_keypress.html
21      https://cloud.r-project.org/web/checks/check_results_keyring.html
22        https://cloud.r-project.org/web/checks/check_results_liteq.html
23      https://cloud.r-project.org/web/checks/check_results_lpSolve.html
24    https://cloud.r-project.org/web/checks/check_results_oskeyring.html
25          https://cloud.r-project.org/web/checks/check_results_pak.html
26    https://cloud.r-project.org/web/checks/check_results_parsedate.html
27        https://cloud.r-project.org/web/checks/check_results_pingr.html
28     https://cloud.r-project.org/web/checks/check_results_pkgcache.html
29    https://cloud.r-project.org/web/checks/check_results_pkgconfig.html
30    https://cloud.r-project.org/web/checks/check_results_pkgsearch.html
31       https://cloud.r-project.org/web/checks/check_results_praise.html
32      https://cloud.r-project.org/web/checks/check_results_presser.html
33   https://cloud.r-project.org/web/checks/check_results_prettycode.html
34  https://cloud.r-project.org/web/checks/check_results_prettyunits.html
35     https://cloud.r-project.org/web/checks/check_results_processx.html
36     https://cloud.r-project.org/web/checks/check_results_progress.html
37           https://cloud.r-project.org/web/checks/check_results_ps.html
38    https://cloud.r-project.org/web/checks/check_results_rcmdcheck.html
39     https://cloud.r-project.org/web/checks/check_results_rcorpora.html
40     https://cloud.r-project.org/web/checks/check_results_rematch2.html
41         https://cloud.r-project.org/web/checks/check_results_rhub.html
42    https://cloud.r-project.org/web/checks/check_results_rversions.html
43       https://cloud.r-project.org/web/checks/check_results_sankey.html
44       https://cloud.r-project.org/web/checks/check_results_secret.html
45  https://cloud.r-project.org/web/checks/check_results_sessioninfo.html
46    https://cloud.r-project.org/web/checks/check_results_showimage.html
47       https://cloud.r-project.org/web/checks/check_results_tracer.html
48    https://cloud.r-project.org/web/checks/check_results_webdriver.html
49     https://cloud.r-project.org/web/checks/check_results_webfakes.html
50       https://cloud.r-project.org/web/checks/check_results_whoami.html
51 https://cloud.r-project.org/web/checks/check_results_xmlparsedata.html
52        https://cloud.r-project.org/web/checks/check_results_xopen.html
53          https://cloud.r-project.org/web/checks/check_results_zip.html
       check_result version
1            OK, 12      NA
2            OK, 12      NA
3            OK, 12      NA
4            OK, 12      NA
5            OK, 12      NA
6            OK, 12      NA
7            OK, 12      NA
8            OK, 12      NA
9            OK, 12      NA
10           OK, 12      NA
11 ERROR, OK, 1, 11      NA
12           OK, 12      NA
13           OK, 12      NA
14           OK, 12      NA
15           OK, 12      NA
16           OK, 12      NA
17   NOTE, OK, 7, 5      NA
18           OK, 12      NA
19           OK, 12      NA
20           OK, 12      NA
21  NOTE, OK, 2, 10      NA
22           OK, 12      NA
23           OK, 12      NA
24           OK, 12      NA
25  NOTE, OK, 1, 11      NA
26           OK, 12      NA
27           OK, 12      NA
28 ERROR, OK, 1, 11      NA
29           OK, 12      NA
30           OK, 12      NA
31           OK, 12      NA
32 ERROR, OK, 1, 11      NA
33           OK, 12      NA
34           OK, 12      NA
35           OK, 12      NA
36           OK, 12      NA
37           OK, 12      NA
38  NOTE, OK, 1, 11      NA
39   NOTE, OK, 7, 5      NA
40  NOTE, OK, 1, 11      NA
41           OK, 12      NA
42           OK, 12      NA
43           OK, 12      NA
44           OK, 12      NA
45           OK, 12      NA
46           OK, 12      NA
47           OK, 12      NA
48           OK, 12      NA
49 ERROR, OK, 1, 11      NA
50           OK, 12      NA
51           OK, 12      NA
52           OK, 12      NA
53 ERROR, OK, 1, 11      NA

Packages

Get packages based checks.

Function of the cchecks R package: cch_pkgs().

Packages

Get checks data for all packages.

GET [/pkgs] Default limit of 10, max of 1000.

Example with custom offset and limit.

curl https://cranchecks.info/pkgs/?offset=5&limit=2 | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 6793
date: Mon, 18 Jan 2021 13:34:38 GMT

{
    "found": 18224,
    "count": 2,
    "offset": 5,
    "error": null,
    "data": [
        {
            "package": "ibb",
            "url": "https://cloud.r-project.org/web/checks/check_results_ibb.html",
            "date_updated": "2021-01-18T12:04:52.139Z",
            "summary": {
                "any": true,
                "ok": 7,
                "note": 5,
                "warn": 0,
                "error": 0,
                "fail": 0
            },
            "checks": [
                {
                    "flavor": "r-devel-linux-x86_64-debian-clang",
                    "version": "0.0.1",
                    "tinstall": 3.64,
                    "tcheck": 39.93,
                    "ttotal": 43.57,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/ibb-00check.html"
                },
                {
                    "flavor": "r-devel-linux-x86_64-debian-gcc",
                    "version": "0.0.1",
                    "tinstall": 2.95,
                    "tcheck": 30.09,
                    "ttotal": 33.04,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-gcc/ibb-00check.html"
                },
                {
                    "flavor": "r-devel-linux-x86_64-fedora-clang",
                    "version": "0.0.1",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 61.04,
                    "status": "NOTE",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-clang/ibb-00check.html"
                },
                {
                    "flavor": "r-devel-linux-x86_64-fedora-gcc",
                    "version": "0.0.1",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 50.06,
                    "status": "NOTE",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-gcc/ibb-00check.html"
                },
                {
                    "flavor": "r-devel-windows-ix86+x86_64",
                    "version": "0.0.1",
                    "tinstall": 7,
                    "tcheck": 45,
                    "ttotal": 52,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-windows-ix86+x86_64/ibb-00check.html"
                },
                {
                    "flavor": "r-patched-linux-x86_64",
                    "version": "0.0.1",
                    "tinstall": 3.66,
                    "tcheck": 37.57,
                    "ttotal": 41.23,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-patched-linux-x86_64/ibb-00check.html"
                },
                {
                    "flavor": "r-patched-solaris-x86",
                    "version": "0.0.1",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 68.3,
                    "status": "NOTE",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-patched-solaris-x86/ibb-00check.html"
                },
                {
                    "flavor": "r-release-linux-x86_64",
                    "version": "0.0.1",
                    "tinstall": 3.05,
                    "tcheck": 37.32,
                    "ttotal": 40.37,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-release-linux-x86_64/ibb-00check.html"
                },
                {
                    "flavor": "r-release-macos-x86_64",
                    "version": "0.0.1",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 0,
                    "status": "NOTE",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-release-macos-x86_64/ibb-00check.html"
                },
                {
                    "flavor": "r-release-windows-ix86+x86_64",
                    "version": "0.0.1",
                    "tinstall": 8,
                    "tcheck": 44,
                    "ttotal": 52,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-release-windows-ix86+x86_64/ibb-00check.html"
                },
                {
                    "flavor": "r-oldrel-macos-x86_64",
                    "version": "0.0.1",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 0,
                    "status": "NOTE",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-oldrel-macos-x86_64/ibb-00check.html"
                },
                {
                    "flavor": "r-oldrel-windows-ix86+x86_64",
                    "version": "0.0.1",
                    "tinstall": 7,
                    "tcheck": 37,
                    "ttotal": 44,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-oldrel-windows-ix86+x86_64/ibb-00check.html"
                }
            ],
            "check_details": {
                "details": [
                    {
                        "version": "0.0.1",
                        "output": "Namespace in Imports field not imported from: β€˜xml2’\n  All declared Imports should be used.",
                        "check": "dependencies in R code",
                        "flavors": [
                            "r-devel-linux-x86_64-fedora-clang",
                            "r-devel-linux-x86_64-fedora-gcc",
                            "r-patched-solaris-x86",
                            "r-release-macos-x86_64",
                            "r-oldrel-macos-x86_64"
                        ]
                    },
                    {
                        "version": "0.0.1",
                        "output": "  Note: found 359 marked UTF-8 strings",
                        "check": "data for non-ASCII characters",
                        "flavors": [
                            "r-devel-linux-x86_64-fedora-clang",
                            "r-devel-linux-x86_64-fedora-gcc",
                            "r-patched-solaris-x86",
                            "r-release-macos-x86_64",
                            "r-oldrel-macos-x86_64"
                        ]
                    }
                ],
                "additional_issues": null
            }
        },
        {
            "package": "ChIPtest",
            "url": "https://cloud.r-project.org/web/checks/check_results_ChIPtest.html",
            "date_updated": "2021-01-18T12:04:52.139Z",
            "summary": {
                "any": true,
                "ok": 10,
                "note": 2,
                "warn": 0,
                "error": 0,
                "fail": 0
            },
            "checks": [
                {
                    "flavor": "r-devel-linux-x86_64-debian-clang",
                    "version": "1.0",
                    "tinstall": 3.3,
                    "tcheck": 25.26,
                    "ttotal": 28.56,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-devel-linux-x86_64-debian-gcc",
                    "version": "1.0",
                    "tinstall": 2.27,
                    "tcheck": 20.5,
                    "ttotal": 22.77,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-gcc/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-devel-linux-x86_64-fedora-clang",
                    "version": "1.0",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 45.65,
                    "status": "NOTE",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-clang/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-devel-linux-x86_64-fedora-gcc",
                    "version": "1.0",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 37.62,
                    "status": "NOTE",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-gcc/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-devel-windows-ix86+x86_64",
                    "version": "1.0",
                    "tinstall": 8,
                    "tcheck": 41,
                    "ttotal": 49,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-windows-ix86+x86_64/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-patched-linux-x86_64",
                    "version": "1.0",
                    "tinstall": 2.76,
                    "tcheck": 24.79,
                    "ttotal": 27.55,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-patched-linux-x86_64/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-patched-solaris-x86",
                    "version": "1.0",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 56.7,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-patched-solaris-x86/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-release-linux-x86_64",
                    "version": "1.0",
                    "tinstall": 2.75,
                    "tcheck": 24.87,
                    "ttotal": 27.62,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-release-linux-x86_64/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-release-macos-x86_64",
                    "version": "1.0",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 0,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-release-macos-x86_64/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-release-windows-ix86+x86_64",
                    "version": "1.0",
                    "tinstall": 8,
                    "tcheck": 53,
                    "ttotal": 61,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-release-windows-ix86+x86_64/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-oldrel-macos-x86_64",
                    "version": "1.0",
                    "tinstall": 0,
                    "tcheck": 0,
                    "ttotal": 0,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-oldrel-macos-x86_64/ChIPtest-00check.html"
                },
                {
                    "flavor": "r-oldrel-windows-ix86+x86_64",
                    "version": "1.0",
                    "tinstall": 16,
                    "tcheck": 63,
                    "ttotal": 79,
                    "status": "OK",
                    "check_url": "https://www.R-project.org/nosvn/R.check/r-oldrel-windows-ix86+x86_64/ChIPtest-00check.html"
                }
            ],
            "check_details": {
                "details": [
                    {
                        "version": "1.0",
                        "output": "File β€˜ChIPtest/libs/ChIPtest.so’:\n  Found no calls to: β€˜R_registerRoutines’, β€˜R_useDynamicSymbols’\n\nIt is good practice to register native routines and to disable symbol\nsearch.\n\nSee β€˜Writing portable packages’ in the β€˜Writing R Extensions’ manual.",
                        "check": "compiled code",
                        "flavors": [
                            "r-devel-linux-x86_64-fedora-clang",
                            "r-devel-linux-x86_64-fedora-gcc"
                        ]
                    }
                ],
                "additional_issues": null
            }
        }
    ]
}
cchecks::cch_pkgs(
  limit = 2, 
  offset = 5
  )
$found
[1] 18224

$count
[1] 2

$offset
[1] 5

$error
NULL

$data
   package                                                                url
1      ibb      https://cloud.r-project.org/web/checks/check_results_ibb.html
2 ChIPtest https://cloud.r-project.org/web/checks/check_results_ChIPtest.html
              date_updated summary.any summary.ok summary.note summary.warn
1 2021-01-18T12:04:52.139Z        TRUE          7            5            0
2 2021-01-18T12:04:52.139Z        TRUE         10            2            0
  summary.error summary.fail
1             0            0
2             0            0
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          checks
1                              r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 0.0.1, 3.64, 2.95, 0, 0, 7, 3.66, 0, 3.05, 0, 8, 0, 7, 39.93, 30.09, 0, 0, 45, 37.57, 0, 37.32, 0, 44, 0, 37, 43.57, 33.04, 61.04, 50.06, 52, 41.23, 68.3, 40.37, 0, 52, 0, 44, OK, OK, NOTE, NOTE, OK, OK, NOTE, OK, NOTE, OK, NOTE, OK, https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-gcc/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-clang/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-gcc/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-devel-windows-ix86+x86_64/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-patched-linux-x86_64/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-patched-solaris-x86/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-release-linux-x86_64/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-release-macos-x86_64/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-release-windows-ix86+x86_64/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-oldrel-macos-x86_64/ibb-00check.html, https://www.R-project.org/nosvn/R.check/r-oldrel-windows-ix86+x86_64/ibb-00check.html
2 r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.3, 2.27, 0, 0, 8, 2.76, 0, 2.75, 0, 8, 0, 16, 25.26, 20.5, 0, 0, 41, 24.79, 0, 24.87, 0, 53, 0, 63, 28.56, 22.77, 45.65, 37.62, 49, 27.55, 56.7, 27.62, 0, 61, 0, 79, OK, OK, NOTE, NOTE, OK, OK, OK, OK, OK, OK, OK, OK, https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-gcc/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-clang/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-gcc/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-devel-windows-ix86+x86_64/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-patched-linux-x86_64/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-patched-solaris-x86/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-release-linux-x86_64/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-release-macos-x86_64/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-release-windows-ix86+x86_64/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-oldrel-macos-x86_64/ChIPtest-00check.html, https://www.R-project.org/nosvn/R.check/r-oldrel-windows-ix86+x86_64/ChIPtest-00check.html
                                                                                                                                                                                                                                                                                                                                                                                                                                                                          check_details.details
1 0.0.1, 0.0.1, Namespace in Imports field not imported from: β€˜xml2’\n  All declared Imports should be used.,   Note: found 359 marked UTF-8 strings, dependencies in R code, data for non-ASCII characters, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-macos-x86_64, r-oldrel-macos-x86_64, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-macos-x86_64, r-oldrel-macos-x86_64
2                                                                                                                                        1.0, File β€˜ChIPtest/libs/ChIPtest.so’:\n  Found no calls to: β€˜R_registerRoutines’, β€˜R_useDynamicSymbols’\n\nIt is good practice to register native routines and to disable symbol\nsearch.\n\nSee β€˜Writing portable packages’ in the β€˜Writing R Extensions’ manual., compiled code, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
  check_details.additional_issues
1                              NA
2                              NA

Packages by name

Get checks summary by package name.

Function of the cchecks package: cch_pkgs_history()

GET [/pkgs/{package}]

curl https://cranchecks.info/pkgs/rhub | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 2846
date: Mon, 18 Jan 2021 13:34:39 GMT

{
    "error": null,
    "data": {
        "package": "rhub",
        "url": "https://cloud.r-project.org/web/checks/check_results_rhub.html",
        "date_updated": "2021-01-18T12:04:52.363Z",
        "summary": {
            "any": false,
            "ok": 12,
            "note": 0,
            "warn": 0,
            "error": 0,
            "fail": 0
        },
        "checks": [
            {
                "flavor": "r-devel-linux-x86_64-debian-clang",
                "version": "1.1.1",
                "tinstall": 7.21,
                "tcheck": 50.75,
                "ttotal": 57.96,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/rhub-00check.html"
            },
            {
                "flavor": "r-devel-linux-x86_64-debian-gcc",
                "version": "1.1.1",
                "tinstall": 5.98,
                "tcheck": 39.64,
                "ttotal": 45.62,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-gcc/rhub-00check.html"
            },
            {
                "flavor": "r-devel-linux-x86_64-fedora-clang",
                "version": "1.1.1",
                "tinstall": 0,
                "tcheck": 0,
                "ttotal": 78.72,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-clang/rhub-00check.html"
            },
            {
                "flavor": "r-devel-linux-x86_64-fedora-gcc",
                "version": "1.1.1",
                "tinstall": 0,
                "tcheck": 0,
                "ttotal": 83.64,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-gcc/rhub-00check.html"
            },
            {
                "flavor": "r-devel-windows-ix86+x86_64",
                "version": "1.1.1",
                "tinstall": 12,
                "tcheck": 61,
                "ttotal": 73,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-windows-ix86+x86_64/rhub-00check.html"
            },
            {
                "flavor": "r-patched-linux-x86_64",
                "version": "1.1.1",
                "tinstall": 7.81,
                "tcheck": 48.09,
                "ttotal": 55.9,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-patched-linux-x86_64/rhub-00check.html"
            },
            {
                "flavor": "r-patched-solaris-x86",
                "version": "1.1.1",
                "tinstall": 0,
                "tcheck": 0,
                "ttotal": 97.4,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-patched-solaris-x86/rhub-00check.html"
            },
            {
                "flavor": "r-release-linux-x86_64",
                "version": "1.1.1",
                "tinstall": 6.16,
                "tcheck": 48.8,
                "ttotal": 54.96,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-release-linux-x86_64/rhub-00check.html"
            },
            {
                "flavor": "r-release-macos-x86_64",
                "version": "1.1.1",
                "tinstall": 0,
                "tcheck": 0,
                "ttotal": 0,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-release-macos-x86_64/rhub-00check.html"
            },
            {
                "flavor": "r-release-windows-ix86+x86_64",
                "version": "1.1.1",
                "tinstall": 12,
                "tcheck": 60,
                "ttotal": 72,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-release-windows-ix86+x86_64/rhub-00check.html"
            },
            {
                "flavor": "r-oldrel-macos-x86_64",
                "version": "1.1.1",
                "tinstall": 0,
                "tcheck": 0,
                "ttotal": 0,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-oldrel-macos-x86_64/rhub-00check.html"
            },
            {
                "flavor": "r-oldrel-windows-ix86+x86_64",
                "version": "1.1.1",
                "tinstall": 11,
                "tcheck": 51,
                "ttotal": 62,
                "status": "OK",
                "check_url": "https://www.R-project.org/nosvn/R.check/r-oldrel-windows-ix86+x86_64/rhub-00check.html"
            }
        ],
        "check_details": null
    }
}
cchecks::cch_pkgs(x = "rhub")
$error
NULL

$data
$data$package
[1] "rhub"

$data$url
[1] "https://cloud.r-project.org/web/checks/check_results_rhub.html"

$data$date_updated
[1] "2021-01-18T12:04:52.363Z"

$data$summary
$data$summary$any
[1] FALSE

$data$summary$ok
[1] 12

$data$summary$note
[1] 0

$data$summary$warn
[1] 0

$data$summary$error
[1] 0

$data$summary$fail
[1] 0


$data$checks
                              flavor version tinstall tcheck ttotal status
1  r-devel-linux-x86_64-debian-clang   1.1.1     7.21  50.75  57.96     OK
2    r-devel-linux-x86_64-debian-gcc   1.1.1     5.98  39.64  45.62     OK
3  r-devel-linux-x86_64-fedora-clang   1.1.1     0.00   0.00  78.72     OK
4    r-devel-linux-x86_64-fedora-gcc   1.1.1     0.00   0.00  83.64     OK
5        r-devel-windows-ix86+x86_64   1.1.1    12.00  61.00  73.00     OK
6             r-patched-linux-x86_64   1.1.1     7.81  48.09  55.90     OK
7              r-patched-solaris-x86   1.1.1     0.00   0.00  97.40     OK
8             r-release-linux-x86_64   1.1.1     6.16  48.80  54.96     OK
9             r-release-macos-x86_64   1.1.1     0.00   0.00   0.00     OK
10     r-release-windows-ix86+x86_64   1.1.1    12.00  60.00  72.00     OK
11             r-oldrel-macos-x86_64   1.1.1     0.00   0.00   0.00     OK
12      r-oldrel-windows-ix86+x86_64   1.1.1    11.00  51.00  62.00     OK
                                                                                     check_url
1  https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/rhub-00check.html
2    https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-gcc/rhub-00check.html
3  https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-clang/rhub-00check.html
4    https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-gcc/rhub-00check.html
5        https://www.R-project.org/nosvn/R.check/r-devel-windows-ix86+x86_64/rhub-00check.html
6             https://www.R-project.org/nosvn/R.check/r-patched-linux-x86_64/rhub-00check.html
7              https://www.R-project.org/nosvn/R.check/r-patched-solaris-x86/rhub-00check.html
8             https://www.R-project.org/nosvn/R.check/r-release-linux-x86_64/rhub-00check.html
9             https://www.R-project.org/nosvn/R.check/r-release-macos-x86_64/rhub-00check.html
10     https://www.R-project.org/nosvn/R.check/r-release-windows-ix86+x86_64/rhub-00check.html
11             https://www.R-project.org/nosvn/R.check/r-oldrel-macos-x86_64/rhub-00check.html
12      https://www.R-project.org/nosvn/R.check/r-oldrel-windows-ix86+x86_64/rhub-00check.html

$data$check_details
NULL

Packages by name (history)

Get checks history by package name.

GET [/pkgs] Default limit of 10, max of 50.

GET [/pkgs/{package}/history]

curl https://cranchecks.info/pkgs/rhub/history | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 27942
date: Mon, 18 Jan 2021 13:34:39 GMT

{
    "error": null,
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        "package": "rhub",
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                "date_updated": "2021-01-17T15:03:50.000Z",
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                    },
                    {
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                        "ttotal": 0,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/rhub-00check.html"
                    },
                    {
                        "flavor": "r-devel-linux-x86_64-debian-gcc",
                        "version": "1.1.1",
                        "tinstall": 5.51,
                        "tcheck": 39.3,
                        "ttotal": 44.81,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-gcc/rhub-00check.html"
                    },
                    {
                        "flavor": "r-devel-linux-x86_64-fedora-clang",
                        "version": "1.1.1",
                        "tinstall": 0,
                        "tcheck": 0,
                        "ttotal": 78.8,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-clang/rhub-00check.html"
                    },
                    {
                        "flavor": "r-devel-linux-x86_64-fedora-gcc",
                        "version": "1.1.1",
                        "tinstall": 0,
                        "tcheck": 0,
                        "ttotal": 81.92,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-gcc/rhub-00check.html"
                    },
                    {
                        "flavor": "r-devel-windows-ix86+x86_64",
                        "version": "1.1.1",
                        "tinstall": 14,
                        "tcheck": 60,
                        "ttotal": 74,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-devel-windows-ix86+x86_64/rhub-00check.html"
                    },
                    {
                        "flavor": "r-patched-linux-x86_64",
                        "version": "1.1.1",
                        "tinstall": 7.64,
                        "tcheck": 48.28,
                        "ttotal": 55.92,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-patched-linux-x86_64/rhub-00check.html"
                    },
                    {
                        "flavor": "r-patched-solaris-x86",
                        "version": "1.1.1",
                        "tinstall": 0,
                        "tcheck": 0,
                        "ttotal": 98.5,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-patched-solaris-x86/rhub-00check.html"
                    },
                    {
                        "flavor": "r-release-linux-x86_64",
                        "version": "1.1.1",
                        "tinstall": 7.7,
                        "tcheck": 47.87,
                        "ttotal": 55.57,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-release-linux-x86_64/rhub-00check.html"
                    },
                    {
                        "flavor": "r-release-macos-x86_64",
                        "version": "1.1.1",
                        "tinstall": 0,
                        "tcheck": 0,
                        "ttotal": 0,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-release-macos-x86_64/rhub-00check.html"
                    },
                    {
                        "flavor": "r-release-windows-ix86+x86_64",
                        "version": "1.1.1",
                        "tinstall": 15,
                        "tcheck": 71,
                        "ttotal": 86,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-release-windows-ix86+x86_64/rhub-00check.html"
                    },
                    {
                        "flavor": "r-oldrel-macos-x86_64",
                        "version": "1.1.1",
                        "tinstall": 0,
                        "tcheck": 0,
                        "ttotal": 0,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-oldrel-macos-x86_64/rhub-00check.html"
                    },
                    {
                        "flavor": "r-oldrel-windows-ix86+x86_64",
                        "version": "1.1.1",
                        "tinstall": 10,
                        "tcheck": 48,
                        "ttotal": 58,
                        "status": "OK",
                        "check_url": "https://www.R-project.org/nosvn/R.check/r-oldrel-windows-ix86+x86_64/rhub-00check.html"
                    }
                ],
                "check_details": null
            }
        ]
    }
}
cchecks::cch_pkgs_history(x = "rhub")
$error
NULL

$data
$data$package
[1] "rhub"

$data$history
# A tibble: 30 x 4
   date_updated summary$any   $ok $note $warn $error $fail checks
   <chr>        <lgl>       <int> <int> <int>  <int> <int> <list>
 1 2021-01-17T… FALSE          12     0     0      0     0 <df[,…
 2 2021-01-16T… FALSE          12     0     0      0     0 <df[,…
 3 2021-01-15T… TRUE           11     0     0      1     0 <df[,…
 4 2021-01-14T… TRUE           11     0     0      1     0 <df[,…
 5 2021-01-13T… TRUE           11     0     0      1     0 <df[,…
 6 2021-01-12T… FALSE          12     0     0      0     0 <df[,…
 7 2021-01-11T… FALSE          12     0     0      0     0 <df[,…
 8 2021-01-10T… FALSE          12     0     0      0     0 <df[,…
 9 2021-01-09T… FALSE          12     0     0      0     0 <df[,…
10 2021-01-08T… FALSE          12     0     0      0     0 <df[,…
# … with 20 more rows, and 2 more variables: check_details$details <list>,
#   $additional_issues <lgl>

For historical data across packages see the history endpoint.

Badges

Get packages based checks to display in your package README for instance.

In a Markdown-based README to add a badge you just need something like ![alt text](https://cranchecks.info/badges/summary/{package}).

You might want the badge to point at either the CRAN results page or the CRAN checks API page i.e. either

[![Worst CRAN checks result for the rhub package](https://cranchecks.info/badges/worst/rhub)](https://cran.r-project.org/web/checks/check_results_rhub.html) to obtain Badges example, worst for the rhub package.

or

[![Worst CRAN checks result for the rhub package](https://cranchecks.info/badges/worst/rhub)](https://cranchecks.info/pkgs/rhub) to obtain Badges example, worst for the rhub package.

Summary badges

Get badge for CRAN checks summary by package name.

GET [/badges/summary/{package_name}]

[![Summary of CRAN checks results for the rhub package](https://cranchecks.info/badges/summary/rhub)](https://cranchecks.info/pkgs/rhub) gives Summary of CRAN checks results for the rhub package.

curl https://cranchecks.info/badges/summary/rhub
HTTP/2 200 
cache-control: max-age=300, public
content-type: image/svg+xml; charset=utf-8
expires: Mon, 18 Jan 2021 13:39:23 GMT
server: Caddy
x-content-type-options: nosniff
content-length: 855
date: Mon, 18 Jan 2021 13:34:23 GMT

<?xml version="1.0" encoding="UTF-8"?>
<svg xmlns="http://www.w3.org/2000/svg" width="70" height="20">
  <linearGradient id="b" x2="0" y2="100%">
    <stop offset="0" stop-color="#bbb" stop-opacity=".1"/>
    <stop offset="1" stop-opacity=".1"/>
  </linearGradient>
  <mask id="a">
    <rect width="70" height="20" rx="3" fill="#fff"/>
  </mask>
  <g mask="url(#a)">
    <path fill="#555" d="M0 0h43v20H0z"/>
    <path fill="#4c1" d="M43 0h46.5v20H43z"/>
    <path fill="url(#b)" d="M0 0h70v20H0z"/>
  </g>
  <g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
    <text x="21.5" y="15" fill="#010101" fill-opacity=".3">
      CRAN
    </text>
    <text x="21.5" y="14">
      CRAN
    </text>
    <text x="55.5" y="15" fill="#010101" fill-opacity=".3">
      OK
    </text>
    <text x="55.5" y="14">
      OK
    </text>
  </g>
</svg>

Worst badges

Get badge for CRAN checks worst result by package name. 😰

GET [/badges/worst/{package_name}]

[![Worst CRAN checks results for the rhub package](https://cranchecks.info/badges/summary/rhub)](https://cranchecks.info/pkgs/rhub) gives Worst CRAN checks results for the rhub package.

curl https://cranchecks.info/badges/worst/rhub
HTTP/2 200 
cache-control: max-age=300, public
content-type: image/svg+xml; charset=utf-8
expires: Mon, 18 Jan 2021 13:39:24 GMT
server: Caddy
x-content-type-options: nosniff
content-length: 855
date: Mon, 18 Jan 2021 13:34:24 GMT

<?xml version="1.0" encoding="UTF-8"?>
<svg xmlns="http://www.w3.org/2000/svg" width="70" height="20">
  <linearGradient id="b" x2="0" y2="100%">
    <stop offset="0" stop-color="#bbb" stop-opacity=".1"/>
    <stop offset="1" stop-opacity=".1"/>
  </linearGradient>
  <mask id="a">
    <rect width="70" height="20" rx="3" fill="#fff"/>
  </mask>
  <g mask="url(#a)">
    <path fill="#555" d="M0 0h43v20H0z"/>
    <path fill="#4c1" d="M43 0h46.5v20H43z"/>
    <path fill="url(#b)" d="M0 0h70v20H0z"/>
  </g>
  <g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
    <text x="21.5" y="15" fill="#010101" fill-opacity=".3">
      CRAN
    </text>
    <text x="21.5" y="14">
      CRAN
    </text>
    <text x="55.5" y="15" fill="#010101" fill-opacity=".3">
      OK
    </text>
    <text x="55.5" y="14">
      OK
    </text>
  </g>
</svg>

Flavor badges

Get badge for summary of CRAN checks by flavor and package name.

GET [/badges/flavor/{flavor}/{package}]

πŸ’‘ List of platforms/flavors

[![CRAN checks result on r-devel-linux-x86_64-debian-clang for the rhub package](https://cranchecks.info/badges/flavor/r-devel-linux-x86_64-debian-clang/rhub)](https://cranchecks.info/pkgs/rhub) gives CRAN checks result on r-devel-linux-x86_64-debian-clang for the rhub package.

curl https://cranchecks.info/badges/flavor/r-devel-linux-x86_64-debian-clang/rhub
HTTP/2 200 
cache-control: max-age=300, public
content-type: image/svg+xml; charset=utf-8
expires: Mon, 18 Jan 2021 13:39:24 GMT
server: Caddy
x-content-type-options: nosniff
content-length: 855
date: Mon, 18 Jan 2021 13:34:24 GMT

<?xml version="1.0" encoding="UTF-8"?>
<svg xmlns="http://www.w3.org/2000/svg" width="70" height="20">
  <linearGradient id="b" x2="0" y2="100%">
    <stop offset="0" stop-color="#bbb" stop-opacity=".1"/>
    <stop offset="1" stop-opacity=".1"/>
  </linearGradient>
  <mask id="a">
    <rect width="70" height="20" rx="3" fill="#fff"/>
  </mask>
  <g mask="url(#a)">
    <path fill="#555" d="M0 0h43v20H0z"/>
    <path fill="#4c1" d="M43 0h46.5v20H43z"/>
    <path fill="url(#b)" d="M0 0h70v20H0z"/>
  </g>
  <g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
    <text x="21.5" y="15" fill="#010101" fill-opacity=".3">
      CRAN
    </text>
    <text x="21.5" y="14">
      CRAN
    </text>
    <text x="55.5" y="15" fill="#010101" fill-opacity=".3">
      OK
    </text>
    <text x="55.5" y="14">
      OK
    </text>
  </g>
</svg>

History

The /history/:date route allows GET requests only. The route is intended for fetching compressed new-line delimited JSON for an individual date, where all CRAN checks data across all packages is combined.

Parameter:

:date
should be of the form YYYY-MM-DD, the minimal value is 2018-12-18.

A request to /history/:date leads to a redirect (http status 302) and a returned JSON body with a message telling the user to follow the link in the Location response header in case they aren’t familiar with redirects. The link to follow is a temporary Amazon S3 link to the JSON file for the given date.

One can automatically get the link to the JSON file by following the redirect. You can do this in curl with the -L flag, or in R by using the followlocation curl option like followlocation=1.

An important note is the data in the JSON file is NOT valid JSON as an entire entity. Each line of the file IS valid JSON; called newline delimited JSON (NDJSON). You don’t have to worry about these details if you use cchecks::cch_history(), which takes care of downloading the file and reading in the compressed NDJSON. On the command line, you can do e.g, download the file, save to a .json.gz gzip-compressed file extension, then in the next line decompress the file with gzip, then pipe to jq, and use head to get the first 10 lines

curl -vL https://cranchecks.info/history/2020-04-01 > 2020-04-01.json.gz
gzip -dc 2020-04-01.json.gz | jq . | head -n 2
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0*   Trying 52.34.4.0:443...
* Connected to cranchecks.info (52.34.4.0) port 443 (#0)
* ALPN, offering h2
* ALPN, offering http/1.1
* successfully set certificate verify locations:
*  CAfile: /usr/local/etc/openssl@1.1/cert.pem
*  CApath: /usr/local/etc/openssl@1.1/certs
} [5 bytes data]
* TLSv1.3 (OUT), TLS handshake, Client hello (1):
} [512 bytes data]
* TLSv1.3 (IN), TLS handshake, Server hello (2):
{ [122 bytes data]
* TLSv1.3 (IN), TLS handshake, Encrypted Extensions (8):
{ [15 bytes data]
* TLSv1.3 (IN), TLS handshake, Certificate (11):
{ [2264 bytes data]
* TLSv1.3 (IN), TLS handshake, CERT verify (15):
{ [79 bytes data]
* TLSv1.3 (IN), TLS handshake, Finished (20):
{ [36 bytes data]
* TLSv1.3 (OUT), TLS change cipher, Change cipher spec (1):
} [1 bytes data]
* TLSv1.3 (OUT), TLS handshake, Finished (20):
} [36 bytes data]
* SSL connection using TLSv1.3 / TLS_AES_128_GCM_SHA256
* ALPN, server accepted to use h2
* Server certificate:
*  subject: CN=cranchecks.info
*  start date: Jan 16 16:57:32 2021 GMT
*  expire date: Apr 16 16:57:32 2021 GMT
*  subjectAltName: host "cranchecks.info" matched cert's "cranchecks.info"
*  issuer: C=US; O=Let's Encrypt; CN=R3
*  SSL certificate verify ok.

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0* Using HTTP2, server supports multi-use
* Connection state changed (HTTP/2 confirmed)
* Copying HTTP/2 data in stream buffer to connection buffer after upgrade: len=0
} [5 bytes data]
* Using Stream ID: 1 (easy handle 0x7f831800ba00)
} [5 bytes data]
> GET /history/2020-04-01 HTTP/2
> Host: cranchecks.info
> user-agent: curl/7.74.0
> accept: */*
> 
{ [5 bytes data]
* TLSv1.3 (IN), TLS handshake, Newsession Ticket (4):
{ [130 bytes data]
* Connection state changed (MAX_CONCURRENT_STREAMS == 250)!
} [5 bytes data]
< HTTP/2 302 
< access-control-allow-methods: HEAD, GET
< access-control-allow-origin: *
< cache-control: public, must-revalidate, max-age=60
< content-type: application/json; charset=utf8
< location: https://cchecks-history.s3.us-west-2.amazonaws.com/2020-04-01.json.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIE65F6ZPF3JF7JTA%2F20210118%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20210118T133426Z&X-Amz-Expires=900&X-Amz-SignedHeaders=host&X-Amz-Signature=0585f2d7a51cb1a97a702d357e6fedc383ce6725483cd89373054e719c2ab6b2
< server: Caddy
< x-content-type-options: nosniff
< content-length: 101
< date: Mon, 18 Jan 2021 13:34:26 GMT
< 
{ [5 bytes data]
* Ignoring the response-body
{ [101 bytes data]

100   101  100   101    0     0    247      0 --:--:-- --:--:-- --:--:--   246
* Connection #0 to host cranchecks.info left intact
* Issue another request to this URL: 'https://cchecks-history.s3.us-west-2.amazonaws.com/2020-04-01.json.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIE65F6ZPF3JF7JTA%2F20210118%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20210118T133426Z&X-Amz-Expires=900&X-Amz-SignedHeaders=host&X-Amz-Signature=0585f2d7a51cb1a97a702d357e6fedc383ce6725483cd89373054e719c2ab6b2'
*   Trying 52.218.246.145:443...
* Connected to cchecks-history.s3.us-west-2.amazonaws.com (52.218.246.145) port 443 (#1)
* ALPN, offering h2
* ALPN, offering http/1.1
* successfully set certificate verify locations:
*  CAfile: /usr/local/etc/openssl@1.1/cert.pem
*  CApath: /usr/local/etc/openssl@1.1/certs
} [5 bytes data]
* TLSv1.3 (OUT), TLS handshake, Client hello (1):
} [512 bytes data]
* TLSv1.3 (IN), TLS handshake, Server hello (2):
{ [91 bytes data]
* TLSv1.2 (IN), TLS handshake, Certificate (11):
{ [3269 bytes data]
* TLSv1.2 (IN), TLS handshake, Server key exchange (12):
{ [333 bytes data]
* TLSv1.2 (IN), TLS handshake, Server finished (14):
{ [4 bytes data]
* TLSv1.2 (OUT), TLS handshake, Client key exchange (16):
} [70 bytes data]
* TLSv1.2 (OUT), TLS change cipher, Change cipher spec (1):
} [1 bytes data]
* TLSv1.2 (OUT), TLS handshake, Finished (20):
} [16 bytes data]
* TLSv1.2 (IN), TLS handshake, Finished (20):
{ [16 bytes data]
* SSL connection using TLSv1.2 / ECDHE-RSA-AES128-GCM-SHA256
* ALPN, server did not agree to a protocol
* Server certificate:
*  subject: C=US; ST=Washington; L=Seattle; O=Amazon.com, Inc.; CN=*.s3-us-west-2.amazonaws.com
*  start date: Jul 30 00:00:00 2020 GMT
*  expire date: Aug  4 12:00:00 2021 GMT
*  subjectAltName: host "cchecks-history.s3.us-west-2.amazonaws.com" matched cert's "*.s3.us-west-2.amazonaws.com"
*  issuer: C=US; O=DigiCert Inc; OU=www.digicert.com; CN=DigiCert Baltimore CA-2 G2
*  SSL certificate verify ok.
} [5 bytes data]
> GET /2020-04-01.json.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIE65F6ZPF3JF7JTA%2F20210118%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20210118T133426Z&X-Amz-Expires=900&X-Amz-SignedHeaders=host&X-Amz-Signature=0585f2d7a51cb1a97a702d357e6fedc383ce6725483cd89373054e719c2ab6b2 HTTP/1.1
> Host: cchecks-history.s3.us-west-2.amazonaws.com
> User-Agent: curl/7.74.0
> Accept: */*
> 
{ [5 bytes data]
* Mark bundle as not supporting multiuse
< HTTP/1.1 200 OK
< x-amz-id-2: 12y3EzR0dRi4IFR2VZa8kYI3qz5UnnnbMs5gu0NKpwCCw3pZqxkMFqY5nGp7WtbWDBa0aQ4a38Y=
< x-amz-request-id: E62A077A67479BF2
< Date: Mon, 18 Jan 2021 13:34:27 GMT
< Last-Modified: Wed, 01 Apr 2020 16:36:04 GMT
< ETag: "a7d74593672aa52dace5cd9e62db9351"
< Content-Encoding: gzip
< Accept-Ranges: bytes
< Content-Type: application/json
< Content-Length: 5180606
< Server: AmazonS3
< 
{ [5 bytes data]

  5 5059k    5  270k    0     0   241k      0  0:00:20  0:00:01  0:00:19  241k
100 5059k  100 5059k    0     0  3588k      0  0:00:01  0:00:01 --:--:-- 16.2M
* Connection #1 to host cchecks-history.s3.us-west-2.amazonaws.com left intact
{
  "package": "localIV",

⚠️ πŸ’‘ For historical data by package refer to the package history endpoint.

cchecks::cch_history("2020-04-01")
# opening file input connection.

 Found 500 records...
 Found 1000 records...
 Found 1500 records...
 Found 2000 records...
 Found 2500 records...
 Found 3000 records...
 Found 3500 records...
 Found 4000 records...
 Found 4500 records...
 Found 5000 records...
 Found 5500 records...
 Found 6000 records...
 Found 6500 records...
 Found 7000 records...
 Found 7500 records...
 Found 8000 records...
 Found 8500 records...
 Found 9000 records...
 Found 9500 records...
 Found 10000 records...
 Found 10500 records...
 Found 11000 records...
 Found 11500 records...
 Found 12000 records...
 Found 12500 records...
 Found 13000 records...
 Found 13500 records...
 Found 14000 records...
 Found 14500 records...
 Found 15000 records...
 Found 15500 records...
 Found 16000 records...
 Found 16327 records...
 Imported 16327 records. Simplifying...

# closing file input connection.
# A tibble: 16,327 x 5
   package  summary            checks              check_details             date_updated 
   <chr>    <chr>              <chr>               <chr>                     <chr>        
 1 localIV  "{\"any\":false,\… "[{\"flavor\":\"r-… "null"                    2020-04-01 1…
 2 di       "{\"any\":false,\… "[{\"flavor\":\"r-… "null"                    2020-04-01 1…
 3 GAR      "{\"any\":true,\"… "[{\"flavor\":\"r-… "{\"version\":\"1.1\",\"… 2020-04-01 1…
 4 MetABEL  "{\"any\":true,\"… "[{\"flavor\":\"r-… "{\"version\":\"0.2-0\",… 2020-04-01 1…
 5 quantab… "{\"any\":false,\… "[{\"flavor\":\"r-… "null"                    2020-04-01 1…
 6 ChIPtest "{\"any\":true,\"… "[{\"flavor\":\"r-… "{\"version\":\"1.0\",\"… 2020-04-01 1…
 7 TSF      "{\"any\":false,\… "[{\"flavor\":\"r-… "null"                    2020-04-01 1…
 8 apercu   "{\"any\":true,\"… "[{\"flavor\":\"r-… "{\"version\":\"0.2.4\",… 2020-04-01 1…
 9 SPRT     "{\"any\":true,\"… "[{\"flavor\":\"r-… "{\"version\":\"1.0\",\"… 2020-04-01 1…
10 W2CWM2C  "{\"any\":false,\… "[{\"flavor\":\"r-… "null"                    2020-04-01 1…
# … with 16,317 more rows

Search

Search package history data. This is a full text search ONLY of the output in check_details field.

Function of the cchecks package: cch_pkgs_search()

GET [/search]

Parameters:

q (string)
full text query, e.g, q=memory
package (string)
a package name. limit results to a single package, e.g, package=taxize
one_each (boolean)
if true, return a single result for each package; useful if you want to find out what packages have match a particular query, and don’t care which day that match happened. default: false; e.g., one_each=true
fields (string)
comma separated string with field to return, e.g., fields=package,check_details
limit/offset
see pagination docs. Max of 50.
curl https://cranchecks.info/search?q=memory | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 198862
date: Mon, 18 Jan 2021 13:34:59 GMT

{
    "error": null,
    "count": 1312,
    "returned": 10,
    "data": [
        {
            "package": "allan",
            "date_updated": "2020-12-19T15:03:46.000Z",
            "summary": {
                "any": true,
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                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpw3Qwsc/RLIBS_b627c072bb3a84/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpw3Qwsc/RLIBS_b627c072bb3a84/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpUZ1rpl/RLIBS_af4e41a7340b2/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpUZ1rpl/RLIBS_af4e41a7340b2/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpw3Qwsc/RLIBS_b627c072bb3a84/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpw3Qwsc/RLIBS_b627c072bb3a84/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpUZ1rpl/RLIBS_af4e41a7340b2/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpUZ1rpl/RLIBS_af4e41a7340b2/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "allanVarSelect: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'update'\ngetbestchunksize: no visible global function definition for 'read.csv'\ngetbestchunksize: no visible global function definition for\n  'object.size'\npredictvbiglm: no visible global function definition for 'read.csv'\npredictvbiglm: no visible global function definition for 'predict'\npredictvbiglm: no visible global function definition for\n  'weighted.mean'\nreadinbigdata : <anonymous>: no visible global function definition for\n  'read.csv'\nUndefined global functions or variables:\n  object.size predict read.csv update weighted.mean\nConsider adding\n  importFrom(\"stats\", \"predict\", \"update\", \"weighted.mean\")\n  importFrom(\"utils\", \"object.size\", \"read.csv\")\nto your NAMESPACE file.",
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                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpw3Qwsc/RLIBS_b627c072bb3a84/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpw3Qwsc/RLIBS_b627c072bb3a84/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpqmoeTz/RLIBS_8a5d4049f25a8a/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpqmoeTz/RLIBS_8a5d4049f25a8a/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "allanVarSelect: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'update'\ngetbestchunksize: no visible global function definition for 'read.csv'\ngetbestchunksize: no visible global function definition for\n  'object.size'\npredictvbiglm: no visible global function definition for 'read.csv'\npredictvbiglm: no visible global function definition for 'predict'\npredictvbiglm: no visible global function definition for\n  'weighted.mean'\nreadinbigdata : <anonymous>: no visible global function definition for\n  'read.csv'\nUndefined global functions or variables:\n  object.size predict read.csv update weighted.mean\nConsider adding\n  importFrom(\"stats\", \"predict\", \"update\", \"weighted.mean\")\n  importFrom(\"utils\", \"object.size\", \"read.csv\")\nto your NAMESPACE file.",
                        "check": "R code for possible problems",
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                            "r-devel-linux-x86_64-fedora-gcc",
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                            "r-patched-solaris-x86",
                            "r-release-linux-x86_64",
                            "r-release-macos-x86_64",
                            "r-release-windows-ix86+x86_64",
                            "r-oldrel-macos-x86_64",
                            "r-oldrel-windows-ix86+x86_64"
                        ]
                    },
                    {
                        "version": "1.01",
                        "output": "prepare_Rd: allan-package.Rd:49-51: Dropping empty section \\seealso\nprepare_Rd: allanVarSelect.Rd:59-60: Dropping empty section \\details\nprepare_Rd: allanVarSelect.Rd:66-67: Dropping empty section \\references\nprepare_Rd: allanVarSelect.Rd:78-79: Dropping empty section \\seealso\nprepare_Rd: fitvbiglm.Rd:38-39: Dropping empty section \\details\nprepare_Rd: fitvbiglm.Rd:49-50: Dropping empty section \\note\nprepare_Rd: fitvbiglm.Rd:43-44: Dropping empty section \\references\nprepare_Rd: fitvbiglm.Rd:53-54: Dropping empty section \\seealso\nprepare_Rd: getbestchunksize.Rd:35-36: Dropping empty section \\details\nprepare_Rd: getbestchunksize.Rd:46-47: Dropping empty section \\note\nprepare_Rd: getbestchunksize.Rd:40-41: Dropping empty section \\references\nprepare_Rd: getbestchunksize.Rd:50-51: Dropping empty section \\seealso\nprepare_Rd: predictvbiglm.Rd:44-45: Dropping empty section \\details\nprepare_Rd: predictvbiglm.Rd:56-57: Dropping empty section \\note\nprepare_Rd: predictvbiglm.Rd:50-51: Dropping empty section \\references\nprepare_Rd: predictvbiglm.Rd:60-61: Dropping empty section \\seealso\nprepare_Rd: readinbigdata.Rd:42-43: Dropping empty section \\note\nprepare_Rd: readinbigdata.Rd:45-46: Dropping empty section \\seealso",
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                            "r-oldrel-windows-ix86+x86_64"
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                    {
                        "version": "1.01",
                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
                        "check": "Rd line widths",
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                            "r-devel-linux-x86_64-fedora-gcc",
                            "r-patched-linux-x86_64",
                            "r-release-linux-x86_64"
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpqmoeTz/RLIBS_8a5d4049f25a8a/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpqmoeTz/RLIBS_8a5d4049f25a8a/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "allanVarSelect: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'update'\ngetbestchunksize: no visible global function definition for 'read.csv'\ngetbestchunksize: no visible global function definition for\n  'object.size'\npredictvbiglm: no visible global function definition for 'read.csv'\npredictvbiglm: no visible global function definition for 'predict'\npredictvbiglm: no visible global function definition for\n  'weighted.mean'\nreadinbigdata : <anonymous>: no visible global function definition for\n  'read.csv'\nUndefined global functions or variables:\n  object.size predict read.csv update weighted.mean\nConsider adding\n  importFrom(\"stats\", \"predict\", \"update\", \"weighted.mean\")\n  importFrom(\"utils\", \"object.size\", \"read.csv\")\nto your NAMESPACE file.",
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                        "output": "prepare_Rd: allan-package.Rd:49-51: Dropping empty section \\seealso\nprepare_Rd: allanVarSelect.Rd:59-60: Dropping empty section \\details\nprepare_Rd: allanVarSelect.Rd:66-67: Dropping empty section \\references\nprepare_Rd: allanVarSelect.Rd:78-79: Dropping empty section \\seealso\nprepare_Rd: fitvbiglm.Rd:38-39: Dropping empty section \\details\nprepare_Rd: fitvbiglm.Rd:49-50: Dropping empty section \\note\nprepare_Rd: fitvbiglm.Rd:43-44: Dropping empty section \\references\nprepare_Rd: fitvbiglm.Rd:53-54: Dropping empty section \\seealso\nprepare_Rd: getbestchunksize.Rd:35-36: Dropping empty section \\details\nprepare_Rd: getbestchunksize.Rd:46-47: Dropping empty section \\note\nprepare_Rd: getbestchunksize.Rd:40-41: Dropping empty section \\references\nprepare_Rd: getbestchunksize.Rd:50-51: Dropping empty section \\seealso\nprepare_Rd: predictvbiglm.Rd:44-45: Dropping empty section \\details\nprepare_Rd: predictvbiglm.Rd:56-57: Dropping empty section \\note\nprepare_Rd: predictvbiglm.Rd:50-51: Dropping empty section \\references\nprepare_Rd: predictvbiglm.Rd:60-61: Dropping empty section \\seealso\nprepare_Rd: readinbigdata.Rd:42-43: Dropping empty section \\note\nprepare_Rd: readinbigdata.Rd:45-46: Dropping empty section \\seealso",
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                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
                        "check": "Rd line widths",
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                            "r-devel-linux-x86_64-fedora-gcc",
                            "r-patched-linux-x86_64",
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
                        "flavors": [
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
                        "flavors": [
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpqmoeTz/RLIBS_8a5d4049f25a8a/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpqmoeTz/RLIBS_8a5d4049f25a8a/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "allanVarSelect: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'update'\ngetbestchunksize: no visible global function definition for 'read.csv'\ngetbestchunksize: no visible global function definition for\n  'object.size'\npredictvbiglm: no visible global function definition for 'read.csv'\npredictvbiglm: no visible global function definition for 'predict'\npredictvbiglm: no visible global function definition for\n  'weighted.mean'\nreadinbigdata : <anonymous>: no visible global function definition for\n  'read.csv'\nUndefined global functions or variables:\n  object.size predict read.csv update weighted.mean\nConsider adding\n  importFrom(\"stats\", \"predict\", \"update\", \"weighted.mean\")\n  importFrom(\"utils\", \"object.size\", \"read.csv\")\nto your NAMESPACE file.",
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                        "version": "1.01",
                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
                        "check": "Rd line widths",
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                            "r-devel-linux-x86_64-fedora-clang",
                            "r-devel-linux-x86_64-fedora-gcc",
                            "r-patched-linux-x86_64",
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
                        "flavors": [
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpqmoeTz/RLIBS_8a5d4049f25a8a/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpqmoeTz/RLIBS_8a5d4049f25a8a/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmp0gTt97/RLIBS_307427d72119/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmp0gTt97/RLIBS_307427d72119/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmp0gTt97/RLIBS_307427d72119/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmp0gTt97/RLIBS_307427d72119/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmp0gTt97/RLIBS_307427d72119/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmp0gTt97/RLIBS_307427d72119/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
                        "check": "Rd line widths",
                        "flavors": [
                            "r-devel-linux-x86_64-debian-clang",
                            "r-devel-linux-x86_64-debian-gcc",
                            "r-devel-linux-x86_64-fedora-clang",
                            "r-devel-linux-x86_64-fedora-gcc",
                            "r-patched-linux-x86_64",
                            "r-release-linux-x86_64"
                        ]
                    },
                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpuEtbIA/RLIBS_7e4e3c4b9170b5/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
                        "flavors": [
                            "r-devel-windows-ix86+x86_64"
                        ]
                    },
                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpMPpWqD/RLIBS_234cf04a0154d3/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
                        "flavors": [
                            "r-release-windows-ix86+x86_64"
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                    },
                    {
                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmp0gTt97/RLIBS_307427d72119/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmp0gTt97/RLIBS_307427d72119/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
                        "flavors": [
                            "r-oldrel-windows-ix86+x86_64"
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                "additional_issues": null
            }
        }
    ]
}
cchecks::cch_pkgs_search(q = "memory")
$error
NULL

$count
[1] 1312

$returned
[1] 30

$data
# A tibble: 30 x 5
   package date_updated summary$any   $ok $note $warn $error $fail checks
   <chr>   <chr>        <lgl>       <int> <int> <int>  <int> <int> <list>
 1 allan   2020-12-19T… TRUE            0     9     0      3     0 <df[,…
 2 allan   2020-12-20T… TRUE            0     9     0      3     0 <df[,…
 3 allan   2020-12-21T… TRUE            0     9     0      3     0 <df[,…
 4 allan   2020-12-22T… TRUE            0     9     0      3     0 <df[,…
 5 allan   2020-12-23T… TRUE            0     9     0      3     0 <df[,…
 6 allan   2020-12-24T… TRUE            0     9     0      3     0 <df[,…
 7 allan   2020-12-25T… TRUE            0     9     0      3     0 <df[,…
 8 allan   2020-12-26T… TRUE            0     9     0      3     0 <df[,…
 9 allan   2020-12-27T… TRUE            0     9     0      3     0 <df[,…
10 allan   2020-12-28T… TRUE            0     9     0      3     0 <df[,…
# … with 20 more rows, and 2 more variables: check_details$details <list>,
#   $additional_issues <lgl>

curl https://cranchecks.info/search?q=memory&one_each=true&limit=2 | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 54979
date: Mon, 18 Jan 2021 13:35:18 GMT

{
    "error": null,
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    "returned": 2,
    "data": [
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            "check_details": {
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                        "version": "1.01",
                        "output": "Malformed Description field: should contain one or more complete sentences.",
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                            "r-oldrel-macos-x86_64",
                            "r-oldrel-windows-ix86+x86_64"
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                    },
                    {
                        "version": "1.01",
                        "output": "allanVarSelect: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'read.csv'\nfitvbiglm: no visible global function definition for 'update'\ngetbestchunksize: no visible global function definition for 'read.csv'\ngetbestchunksize: no visible global function definition for\n  'object.size'\npredictvbiglm: no visible global function definition for 'read.csv'\npredictvbiglm: no visible global function definition for 'predict'\npredictvbiglm: no visible global function definition for\n  'weighted.mean'\nreadinbigdata : <anonymous>: no visible global function definition for\n  'read.csv'\nUndefined global functions or variables:\n  object.size predict read.csv update weighted.mean\nConsider adding\n  importFrom(\"stats\", \"predict\", \"update\", \"weighted.mean\")\n  importFrom(\"utils\", \"object.size\", \"read.csv\")\nto your NAMESPACE file.",
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                            "r-oldrel-macos-x86_64",
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                    {
                        "version": "1.01",
                        "output": "Rd file 'allan-package.Rd':\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)\n\nRd file 'allanVarSelect.Rd':\n  \\usage lines wider than 90 characters:\n     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = \"AIC\", currentchunksize = -1, si ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'fitvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n\nRd file 'getbestchunksize.Rd':\n  \\usage lines wider than 90 characters:\n     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)\n  \\examples lines wider than 100 characters:\n             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory\n             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead\n\nRd file 'predictvbiglm.Rd':\n  \\usage lines wider than 90 characters:\n     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]\n  \\examples lines wider than 100 characters:\n     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]\n                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]\n                             weightvector<-as.vector(eval(parse(text=paste(\"CurrentDataSet\",\"$\",weightname,sep=\"\"))))\n                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)\n\nRd file 'readinbigdata.Rd':\n  \\examples lines wider than 100 characters:\n             #The return value is either the next chunk of data or NULL if there is no additional data left.\n             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.\n\nThese lines will be truncated in the PDF manual.",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpw3Qwsc/RLIBS_b627c072bb3a84/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpw3Qwsc/RLIBS_b627c072bb3a84/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                            "r-devel-windows-ix86+x86_64"
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                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/Rtmpq4JFjl/RLIBS_705f643cb03201/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
                        "check": "examples",
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                        "version": "1.01",
                        "output": "Running examples in 'allan-Ex.R' failed\nThe error most likely occurred in:\n\n> ### Name: allanVarSelect\n> ### Title: Memory Unlimited Forward Stepwise Variable Selection for Linear\n> ###   Models\n> ### Aliases: allanVarSelect\n> ### Keywords: stepwise linear regression memory\n> \n> ### ** Examples\n> \n> #Get external data.  For your own data skip this next line and replace all\n> #instance of SampleData with \"YourFile.csv\".\n> SampleData=system.file(\"extdata\",\"SampleDataFile.csv\", package = \"allan\")\n> \n> #fit smaller data to biglm object\n> columnnames<-names(read.csv(SampleData, nrows=2,header=TRUE))\n> datafeed<-readinbigdata(SampleData,chunksize=1000,col.names=columnnames)\n> datafeed(TRUE)\n> firstchunk<-datafeed(FALSE)\n> \n> #create a biglm model from the small chunk with all variables that will be consdered\n> #for variable selection.\n> bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)\n> \n> #now run variable selection\n> FinalModel<-allanVarSelect(bigmodel,SampleData,SampleData,NumOfSteps=2,criteria=\"MSE\",silent=FALSE)\n[1] \"Total memory usage for 1000 lines:\"\n110560 bytes\n[1] \"Chunksize for dataframe after adjustment factor:\"\n[1] 429630\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529253.65122942\"      \"1529265.34987705\" \"1.29997019294336e+129\" \n                                                \n                \"cont3\"                     \"1\" \n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Iterating Through Dataset and Updating Coefficients\"\n[1] \"Criteria:MSE\"\n[1] \"Iteration Result\"\n               AICValue                BICValue                MSEValue \n     \"1529255.65122942\"      \"1529273.19920087\" \"1.29902541076004e+129\" \n                                                \n                \"cont1\"                     \"2\" \n[1] \"Final Results of Variable Selection:\"\n                 AICValue           BICValue           MSEValue               \nIterationSummary \"1529253.65122942\" \"1529265.34987705\" \"1.29997019294336e+129\"\nIterationSummary \"1529255.65122942\" \"1529273.19920087\" \"1.29902541076004e+129\"\n                            \nIterationSummary \"cont3\" \"1\"\nIterationSummary \"cont1\" \"2\"\n[1] \"Results Stored in $SelectionSummary\"\n> \n> \n> \n> \n> \n> \n> \n> cleanEx()\nWarning in .Internal(gc(verbose, reset, full)) :\n  closing unused connection 3 (D:/temp/RtmpUZ1rpl/RLIBS_af4e41a7340b2/allan/extdata/SampleDataFile.csv)\nError: connections left open:\n\tD:/temp/RtmpUZ1rpl/RLIBS_af4e41a7340b2/allan/extdata/SampleDataFile.csv (file)\nExecution halted",
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                    {
                        "version": "0.15.3",
                        "output": "  Running 'testthat.R' [125s/75s]\nRunning the tests in 'tests/testthat.R' failed.\nComplete output:\n  > library(testthat)\n  > library(antaresViz)\n  Loading required package: antaresRead\n  Loading required package: antaresProcessing\n  \n  Attaching package: 'antaresProcessing'\n  \n  The following object is masked from 'package:testthat':\n  \n      compare\n  \n  Loading required package: spMaps\n  Loading required package: sp\n  Loading required package: raster\n  \n  Attaching package: 'raster'\n  \n  The following object is masked from 'package:antaresProcessing':\n  \n      getValues\n  \n  Loading required package: rgeos\n  rgeos version: 0.5-5, (SVN revision 640)\n   GEOS runtime version: 3.9.0-CAPI-1.16.2 \n   Linking to sp version: 1.4-4 \n   Polygon checking: TRUE \n  \n  > \n  > test_check(\"antaresViz\")\n  == Skipped tests ===============================================================\n  * .runExchangesStackTest is not TRUE (8)\n  * .runPlotMapTest is not TRUE (1)\n  * .runProdStackTest is not TRUE (4)\n  * .runTsPlotTest is not TRUE (1)\n  \n  == Warnings ====================================================================\n  -- Warning (test-exchangesStack.R:5:3): no interactive -------------------------\n  'memory.limit()' is Windows-specific\n  Backtrace:\n   1. antaresRead::readAntares(links = \"all\", timeStep = \"daily\", showProgress = FALSE) test-exchangesStack.R:5:2\n   3. utils::memory.limit()\n  \n  == Failed tests ================================================================\n  -- Error (test-prodStack.R:309:3): prodStack must work with refStudy, if interactive is set to TRUE and if x and refStudy are antaresData,  --\n  Error: $ operator is invalid for atomic vectors\n  Backtrace:\n      x\n   1. \\-antaresViz:::.get_data_from_htmlwidget(PS3) test-prodStack.R:309:2\n  -- Error (test-prodStack.R:338:3): prodStack must work with refStudy, if interactive is set to TRUE and if x a list of antaresData and refStudy an antaresData,  --\n  Error: $ operator is invalid for atomic vectors\n  Backtrace:\n      x\n   1. \\-antaresViz:::.get_data_from_htmlwidget(PS3, widgetsNumber = 2) test-prodStack.R:338:2\n  -- Error (test-prodStack.R:388:3): prodStack must work with refStudy, if interactive is set to TRUE and if x is an antaresDataList and refStudy an antaresDataList,  --\n  Error: $ operator is invalid for atomic vectors\n  Backtrace:\n      x\n   1. \\-antaresViz:::.get_data_from_htmlwidget(PS3, widgetsNumber = 1) test-prodStack.R:388:2\n  -- Error (test-prodStack.R:442:3): prodStack must work with refStudy, if interactive is set to TRUE and if x is a list of antaresDataList and refStudy an antaresDataList ,  --\n  Error: $ operator is invalid for atomic vectors\n  Backtrace:\n      x\n   1. \\-antaresViz:::.get_data_from_htmlwidget(PS3, widgetsNumber = 3) test-prodStack.R:442:2\n  \n  [ FAIL 4 | WARN 1 | SKIP 14 | PASS 287 ]\n  Error: Test failures\n  Execution halted",
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                        "version": "0.15.3",
                        "output": "  Running β€˜testthat.R’ [165s/200s]\nRunning the tests in β€˜tests/testthat.R’ failed.\nComplete output:\n  > library(testthat)\n  > library(antaresViz)\n  Loading required package: antaresRead\n  Loading required package: antaresProcessing\n  \n  Attaching package: 'antaresProcessing'\n  \n  The following object is masked from 'package:testthat':\n  \n      compare\n  \n  Loading required package: spMaps\n  Loading required package: sp\n  Loading required package: raster\n  \n  Attaching package: 'raster'\n  \n  The following object is masked from 'package:antaresProcessing':\n  \n      getValues\n  \n  Loading required package: rgeos\n  rgeos version: 0.5-5, (SVN revision 640)\n   GEOS runtime version: 3.9.0-CAPI-1.16.2 \n   Linking to sp version: 1.4-5 \n   Polygon checking: TRUE \n  \n  > \n  > test_check(\"antaresViz\")\n  ══ Skipped tests ═══════════════════════════════════════════════════════════════\n  ● .runExchangesStackTest is not TRUE (8)\n  ● .runPlotMapTest is not TRUE (1)\n  ● .runProdStackTest is not TRUE (4)\n  ● .runTsPlotTest is not TRUE (1)\n  \n  ══ Warnings ════════════════════════════════════════════════════════════════════\n  ── Warning (test-exchangesStack.R:5:3): no interactive ─────────────────────────\n  'memory.limit()' is Windows-specific\n  Backtrace:\n   1. antaresRead::readAntares(links = \"all\", timeStep = \"daily\", showProgress = FALSE) test-exchangesStack.R:5:2\n   3. utils::memory.limit()\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  Computation failed in `stat_binhex()`:\n    Package `hexbin` required for `stat_binhex`.\n    Please install and try again.\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::ggplotly_build(p)\n    6. plotly:::by_layer(function(l, d) l$compute_statistic(d, layout))\n    7. plotly:::f(l = layers[[i]], d = data[[i]])\n    8. l$compute_statistic(d, layout)\n    9. ggplot2:::f(..., self = self)\n   10. self$stat$compute_layer(data, params, layout)\n   11. ggplot2:::f(..., self = self)\n   12. ggplot2:::dapply(...)\n   13. ggplot2:::apply_fun(df)\n   14. ggplot2:::fun(x, ...)\n   15. base::tryCatch(...)\n   16. base:::tryCatchList(expr, classes, parentenv, handlers)\n   17. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])\n   18. value[[3L]](cond)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n   3. plotly:::ggplotly.ggplot(p)\n   4. plotly::gg2list(...)\n   5. plotly:::layers2traces(data, prestats_data, layout, plot)\n   7. plotly:::to_basic.GeomHex(...)\n   8. ggplot2::resolution(data[[\"x\"]], FALSE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"L",
                        "check": "tests",
                        "flavors": [
                            "r-devel-linux-x86_64-debian-gcc"
                        ]
                    },
                    {
                        "version": "0.15.3",
                        "output": "  Running β€˜testthat.R’ [115s/213s]\nRunning the tests in β€˜tests/testthat.R’ failed.\nComplete output:\n  > library(testthat)\n  > library(antaresViz)\n  Loading required package: antaresRead\n  Loading required package: antaresProcessing\n  \n  Attaching package: 'antaresProcessing'\n  \n  The following object is masked from 'package:testthat':\n  \n      compare\n  \n  Loading required package: spMaps\n  Loading required package: sp\n  Loading required package: raster\n  \n  Attaching package: 'raster'\n  \n  The following object is masked from 'package:antaresProcessing':\n  \n      getValues\n  \n  Loading required package: rgeos\n  rgeos version: 0.5-5, (SVN revision 640)\n   GEOS runtime version: 3.8.1-CAPI-1.13.3 \n   Linking to sp version: 1.4-4 \n   Polygon checking: TRUE \n  \n  > \n  > test_check(\"antaresViz\")\n  ══ Skipped tests ═══════════════════════════════════════════════════════════════\n  ● .runExchangesStackTest is not TRUE (8)\n  ● .runPlotMapTest is not TRUE (1)\n  ● .runProdStackTest is not TRUE (4)\n  ● .runTsPlotTest is not TRUE (1)\n  \n  ══ Warnings ════════════════════════════════════════════════════════════════════\n  ── Warning (test-exchangesStack.R:5:3): no interactive ─────────────────────────\n  'memory.limit()' is Windows-specific\n  Backtrace:\n   1. antaresRead::readAntares(links = \"all\", timeStep = \"daily\", showProgress = FALSE) test-exchangesStack.R:5:2\n   3. utils::memory.limit()\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  Computation failed in `stat_binhex()`:\n    Package `hexbin` required for `stat_binhex`.\n    Please install and try again.\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::ggplotly_build(p)\n    6. plotly:::by_layer(function(l, d) l$compute_statistic(d, layout))\n    7. plotly:::f(l = layers[[i]], d = data[[i]])\n    8. l$compute_statistic(d, layout)\n    9. ggplot2:::f(..., self = self)\n   10. self$stat$compute_layer(data, params, layout)\n   11. ggplot2:::f(..., self = self)\n   12. ggplot2:::dapply(...)\n   13. ggplot2:::apply_fun(df)\n   14. ggplot2:::fun(x, ...)\n   15. base::tryCatch(...)\n   16. base:::tryCatchList(expr, classes, parentenv, handlers)\n   17. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])\n   18. value[[3L]](cond)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n   3. plotly:::ggplotly.ggplot(p)\n   4. plotly::gg2list(...)\n   5. plotly:::layers2traces(data, prestats_data, layout, plot)\n   7. plotly:::to_basic.GeomHex(...)\n   8. ggplot2::resolution(data[[\"x\"]], FALSE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"L",
                        "check": "tests",
                        "flavors": [
                            "r-devel-linux-x86_64-fedora-clang"
                        ]
                    },
                    {
                        "version": "0.15.3",
                        "output": "  Running β€˜testthat.R’ [123s/159s]\nRunning the tests in β€˜tests/testthat.R’ failed.\nComplete output:\n  > library(testthat)\n  > library(antaresViz)\n  Loading required package: antaresRead\n  Loading required package: antaresProcessing\n  \n  Attaching package: 'antaresProcessing'\n  \n  The following object is masked from 'package:testthat':\n  \n      compare\n  \n  Loading required package: spMaps\n  Loading required package: sp\n  Loading required package: raster\n  \n  Attaching package: 'raster'\n  \n  The following object is masked from 'package:antaresProcessing':\n  \n      getValues\n  \n  Loading required package: rgeos\n  rgeos version: 0.5-5, (SVN revision 640)\n   GEOS runtime version: 3.6.4-CAPI-1.10.4 \n   Linking to sp version: 1.4-4 \n   Polygon checking: TRUE \n  \n  > \n  > test_check(\"antaresViz\")\n  ══ Skipped tests ═══════════════════════════════════════════════════════════════\n  ● .runExchangesStackTest is not TRUE (8)\n  ● .runPlotMapTest is not TRUE (1)\n  ● .runProdStackTest is not TRUE (4)\n  ● .runTsPlotTest is not TRUE (1)\n  \n  ══ Warnings ════════════════════════════════════════════════════════════════════\n  ── Warning (test-exchangesStack.R:5:3): no interactive ─────────────────────────\n  'memory.limit()' is Windows-specific\n  Backtrace:\n   1. antaresRead::readAntares(links = \"all\", timeStep = \"daily\", showProgress = FALSE) test-exchangesStack.R:5:2\n   3. utils::memory.limit()\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  Computation failed in `stat_binhex()`:\n    Package `hexbin` required for `stat_binhex`.\n    Please install and try again.\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::ggplotly_build(p)\n    6. plotly:::by_layer(function(l, d) l$compute_statistic(d, layout))\n    7. plotly:::f(l = layers[[i]], d = data[[i]])\n    8. l$compute_statistic(d, layout)\n    9. ggplot2:::f(..., self = self)\n   10. self$stat$compute_layer(data, params, layout)\n   11. ggplot2:::f(..., self = self)\n   12. ggplot2:::dapply(...)\n   13. ggplot2:::apply_fun(df)\n   14. ggplot2:::fun(x, ...)\n   15. base::tryCatch(...)\n   16. base:::tryCatchList(expr, classes, parentenv, handlers)\n   17. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])\n   18. value[[3L]](cond)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n   3. plotly:::ggplotly.ggplot(p)\n   4. plotly::gg2list(...)\n   5. plotly:::layers2traces(data, prestats_data, layout, plot)\n   7. plotly:::to_basic.GeomHex(...)\n   8. ggplot2::resolution(data[[\"x\"]], FALSE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"L",
                        "check": "tests",
                        "flavors": [
                            "r-patched-solaris-x86"
                        ]
                    },
                    {
                        "version": "0.15.3",
                        "output": "  Running β€˜testthat.R’ [129s/135s]\nRunning the tests in β€˜tests/testthat.R’ failed.\nComplete output:\n  > library(testthat)\n  > library(antaresViz)\n  Loading required package: antaresRead\n  Loading required package: antaresProcessing\n  \n  Attaching package: 'antaresProcessing'\n  \n  The following object is masked from 'package:testthat':\n  \n      compare\n  \n  Loading required package: spMaps\n  Loading required package: sp\n  Loading required package: raster\n  \n  Attaching package: 'raster'\n  \n  The following object is masked from 'package:antaresProcessing':\n  \n      getValues\n  \n  Loading required package: rgeos\n  rgeos version: 0.5-5, (SVN revision 640)\n   GEOS runtime version: 3.9.0-CAPI-1.16.2 \n   Linking to sp version: 1.4-5 \n   Polygon checking: TRUE \n  \n  > \n  > test_check(\"antaresViz\")\n  ══ Skipped tests ═══════════════════════════════════════════════════════════════\n  ● .runExchangesStackTest is not TRUE (8)\n  ● .runPlotMapTest is not TRUE (1)\n  ● .runProdStackTest is not TRUE (4)\n  ● .runTsPlotTest is not TRUE (1)\n  \n  ══ Warnings ════════════════════════════════════════════════════════════════════\n  ── Warning (test-exchangesStack.R:5:3): no interactive ─────────────────────────\n  'memory.limit()' is Windows-specific\n  Backtrace:\n   1. antaresRead::readAntares(links = \"all\", timeStep = \"daily\", showProgress = FALSE) test-exchangesStack.R:5:2\n   3. utils::memory.limit()\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  Computation failed in `stat_binhex()`:\n    Package `hexbin` required for `stat_binhex`.\n    Please install and try again.\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::ggplotly_build(p)\n    6. plotly:::by_layer(function(l, d) l$compute_statistic(d, layout))\n    7. plotly:::f(l = layers[[i]], d = data[[i]])\n    8. l$compute_statistic(d, layout)\n    9. ggplot2:::f(..., self = self)\n   10. self$stat$compute_layer(data, params, layout)\n   11. ggplot2:::f(..., self = self)\n   12. ggplot2:::dapply(...)\n   13. ggplot2:::apply_fun(df)\n   14. ggplot2:::fun(x, ...)\n   15. base::tryCatch(...)\n   16. base:::tryCatchList(expr, classes, parentenv, handlers)\n   17. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])\n   18. value[[3L]](cond)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n   3. plotly:::ggplotly.ggplot(p)\n   4. plotly::gg2list(...)\n   5. plotly:::layers2traces(data, prestats_data, layout, plot)\n   7. plotly:::to_basic.GeomHex(...)\n   8. ggplot2::resolution(data[[\"x\"]], FALSE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  ── Warning (test-plotXY.R:6:5): prodStack, no interactive ──────────────────────\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"L",
                        "check": "tests",
                        "flavors": [
                            "r-release-linux-x86_64"
                        ]
                    },
                    {
                        "version": "0.15.3",
                        "output": "  Running 'testthat.R' [100s]\nRunning the tests in 'tests/testthat.R' failed.\nComplete output:\n  > library(testthat)\n  > library(antaresViz)\n  Loading required package: antaresRead\n  Loading required package: antaresProcessing\n  \n  Attaching package: 'antaresProcessing'\n  \n  The following object is masked from 'package:testthat':\n  \n      compare\n  \n  Loading required package: spMaps\n  Loading required package: sp\n  Loading required package: raster\n  \n  Attaching package: 'raster'\n  \n  The following object is masked from 'package:antaresProcessing':\n  \n      getValues\n  \n  Loading required package: rgeos\n  rgeos version: 0.5-5, (SVN revision 640)\n   GEOS runtime version: 3.8.0-CAPI-1.13.1 \n   Linking to sp version: 1.4-5 \n   Polygon checking: TRUE \n  \n  > \n  > test_check(\"antaresViz\")\n  == Skipped tests ===============================================================\n  * .runExchangesStackTest is not TRUE (8)\n  * .runPlotMapTest is not TRUE (1)\n  * .runProdStackTest is not TRUE (4)\n  * .runTsPlotTest is not TRUE (1)\n  \n  == Warnings ====================================================================\n  -- Warning (test-plotXY.R:6:5): prodStack, no interactive ----------------------\n  Computation failed in `stat_binhex()`:\n    Package `hexbin` required for `stat_binhex`.\n    Please install and try again.\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::ggplotly_build(p)\n    6. plotly:::by_layer(function(l, d) l$compute_statistic(d, layout))\n    7. plotly:::f(l = layers[[i]], d = data[[i]])\n    8. l$compute_statistic(d, layout)\n    9. ggplot2:::f(..., self = self)\n   10. self$stat$compute_layer(data, params, layout)\n   11. ggplot2:::f(..., self = self)\n   12. ggplot2:::dapply(...)\n   13. ggplot2:::apply_fun(df)\n   14. ggplot2:::fun(x, ...)\n   15. base::tryCatch(...)\n   16. base:::tryCatchList(expr, classes, parentenv, handlers)\n   17. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])\n   18. value[[3L]](cond)\n  -- Warning (test-plotXY.R:6:5): prodStack, no interactive ----------------------\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  -- Warning (test-plotXY.R:6:5): prodStack, no interactive ----------------------\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"x\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  -- Warning (test-plotXY.R:6:5): prodStack, no interactive ----------------------\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n   3. plotly:::ggplotly.ggplot(p)\n   4. plotly::gg2list(...)\n   5. plotly:::layers2traces(data, prestats_data, layout, plot)\n   7. plotly:::to_basic.GeomHex(...)\n   8. ggplot2::resolution(data[[\"x\"]], FALSE)\n  -- Warning (test-plotXY.R:6:5): prodStack, no interactive ----------------------\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  -- Warning (test-plotXY.R:6:5): prodStack, no interactive ----------------------\n  no non-missing arguments to max; returning -Inf\n  Backtrace:\n    1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n    3. plotly:::ggplotly.ggplot(p)\n    4. plotly::gg2list(...)\n    5. plotly:::layers2traces(data, prestats_data, layout, plot)\n    7. plotly:::to_basic.GeomHex(...)\n    8. ggplot2::resolution(data[[\"y\"]], FALSE)\n   10. base::range(x, na.rm = TRUE)\n  -- Warning (test-plotXY.R:6:5): prodStack, no interactive ----------------------\n  no non-missing arguments to min; returning Inf\n  Backtrace:\n   1. antaresViz::plotXY(dta, \"NODU\", \"LOAD\", precision = 50, sizeOnCount = FALSE) test-plotXY.R:6:4\n   3. plotly:::ggplotly.ggplot(p)\n   4. plotly::gg2list(...)\n   5. plotly:::layers2traces(data, prestats_data, layout, plot)\n   7. plotly:::to_basic.GeomHex(...)\n   8. ggplot2::resolution(data[[\"y\"]], FALSE)\n  \n  == Faile",
                        "check": "tests",
                        "flavors": [
                            "r-release-windows-ix86+x86_64"
                        ]
                    }
                ],
                "additional_issues": null
            }
        }
    ]
}
cchecks::cch_pkgs_search(q = "memory", one_each = TRUE, limit = 2)
$error
NULL

$count
[1] 57

$returned
[1] 2

$data
# A tibble: 2 x 5
  package date_updated summary$any   $ok $note $warn $error $fail checks
  <chr>   <chr>        <lgl>       <int> <int> <int>  <int> <int> <list>
1 allan   2020-12-19T… TRUE            0     9     0      3     0 <df[,…
2 antare… 2021-01-12T… TRUE            6     0     0      6     0 <df[,…
# … with 2 more variables: check_details$details <list>,
#   $additional_issues <lgl>

curl https://cranchecks.info/search?q=memory&one_each=true&fields=package | jq .
HTTP/2 200 
access-control-allow-methods: HEAD, GET
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 1200
date: Mon, 18 Jan 2021 13:35:33 GMT

{
    "error": null,
    "count": 57,
    "returned": 10,
    "data": [
        {
            "package": "allan",
            "date_updated": "2020-12-19T15:03:46.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "antaresViz",
            "date_updated": "2021-01-12T15:03:51.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "aws",
            "date_updated": "2020-12-19T15:03:46.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "batchtools",
            "date_updated": "2020-12-19T15:03:46.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "blockmodeling",
            "date_updated": "2020-12-19T15:03:46.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "bssm",
            "date_updated": "2020-12-19T15:03:46.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "clusternor",
            "date_updated": "2020-12-23T15:03:51.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "compboost",
            "date_updated": "2020-12-19T15:03:46.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "cooccurNet",
            "date_updated": "2020-12-19T15:03:45.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        },
        {
            "package": "ctsemOMX",
            "date_updated": "2020-12-23T15:03:51.000Z",
            "summary": null,
            "checks": null,
            "check_details": null
        }
    ]
}
cchecks::cch_pkgs_search(q = "memory", one_each = TRUE, fields = "package")
$error
NULL

$count
[1] 57

$returned
[1] 30

$data
# A tibble: 30 x 5
   package       date_updated             summary checks check_details
   <chr>         <chr>                    <lgl>   <lgl>  <lgl>        
 1 allan         2020-12-19T15:03:46.000Z NA      NA     NA           
 2 antaresViz    2021-01-12T15:03:51.000Z NA      NA     NA           
 3 aws           2020-12-19T15:03:46.000Z NA      NA     NA           
 4 batchtools    2020-12-19T15:03:46.000Z NA      NA     NA           
 5 blockmodeling 2020-12-19T15:03:46.000Z NA      NA     NA           
 6 bssm          2020-12-19T15:03:46.000Z NA      NA     NA           
 7 clusternor    2020-12-23T15:03:51.000Z NA      NA     NA           
 8 compboost     2020-12-19T15:03:46.000Z NA      NA     NA           
 9 cooccurNet    2020-12-19T15:03:45.000Z NA      NA     NA           
10 ctsemOMX      2020-12-23T15:03:51.000Z NA      NA     NA           
# … with 20 more rows

Notifications

The Notifications endpoint allow you to manage notifications sent to your email address, so you might get aware of failing CRAN checks before CRAN emails you! πŸ’ƒ πŸ”₯

Notifications token

Get a token for CRAN checks notifications.

Function of the cchecks R package: cchn_register().

GET [/notifications/token]

Parameter:

email
email address

The token is sent by email, not returned via the response body or headers!

curl https://cranchecks.info/notifications/token?email=msmaellesalmon@gmail.com | jq .
HTTP/2 200 
content-type: application/json
server: Caddy
x-content-type-options: nosniff
content-length: 43
date: Sat, 16 May 2020 14:12:34 GMT

{
    "error": null,
    "data": "success, email sent"
}
# Interactively
cchecks::cchn_register(email = "jane.doe@cranchecksapidocs.com")

Notifications rules

This section is not about an endpoint, it is information useful for all notifications endpoint. πŸ€“

⚠️ All components of a rule (e.g., status or time) are combined with ‘AND’. That is, if status="warn" and time=3, the rule is “triggered” only if both of status="warn" and time=3 components match the cran checks data.

πŸ’‘ If you want achieve ‘OR’ behavior make multiple rules.

Each rule can have 1 to 4 parameters:

status (str)
match against check status. one of: ok, note, warn, error, fail
time (int)
days in a row the match occurs. an integer. can only go 30 days back (history cleaned up after 30 days)
platforms (str/int)
platform the status occurs on, including negation (e.g., “-solaris”). options: solaris, osx, linux, windows, devel, release, patched, oldrel
regex (str)
a regex to match against the text of an error in check_details.output

Examples in words, and their equivalents as a Ruby hash

ERROR for 3 days in a row across 2 or more platforms
{'status' => 'error', 'time' => 3, 'platforms' => 2, 'regex' => nil}
ERROR for 2 days in a row on all osx platforms
{'status' => 'error', 'time' => 2, 'platforms' => "osx", 'regex' => nil}
ERROR for 2 days in a row on all release R versions
{'status' => 'error', 'time' => 2, 'platforms' => "release", 'regex' => nil}
WARN for 4 days in a row on any platform except Solaris
{'status' => 'warn', 'time' => 4, 'platforms' => "-solaris", 'regex' => nil}
WARN for 2 days in a row across 9 or more platforms
{'status' => 'warn', 'time' => 2, 'platforms' => 10, 'regex' => nil}
NOTE across all osx platforms
{'status' => 'note', 'time' => nil, 'platforms' => "osx", 'regex' => nil}
NOTE
{'status' => 'note', 'time' => nil, 'platforms' => nil, 'regex' => nil}
error details contain regex ‘install’
{'status' => nil, 'time' => nil, 'platforms' => nil, 'regex' => "install"}

Notifications create

Add new notification rules.

Functions of the cchecks R package: cchn_rule_add() and cchn_pkg_rule_add().

curl -XPOST -H "Authorization: Bearer ***" \
 --data '[{"package": "ropenaq", "regex": "install failure"}]' \
 https://cranchecks.info/notifications/rules | jq .
HTTP/2 200 
access-control-allow-methods: POST
access-control-allow-origin: *
cache-control: public, must-revalidate, max-age=60
content-type: application/json; charset=utf8
server: Caddy
x-content-type-options: nosniff
content-length: 131
date: Mon, 18 J