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modelSelection

1.0.7Package record

High-Dimensional Model Selection

2packages depend
3.6Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) doi:10.5281/zenodo.17119597 (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package.

Install

Health

CRAN checks
13OK
Slowest check: 9.3 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-06-09
  • ERROR12 OK, 1 ERROR2026-06-08
  • NOTE12 OK, 1 NOTE2026-06-07
Show 4 earlier changesHide 4 earlier changes
  • OK12 OK2026-04-25
  • NOTE12 OK, 2 NOTE2026-04-22
  • ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
  • NOTE12 OK, 2 NOTE2026-03-10
Advisories

none known

Documentation

In release 1.0.7
READMEYes · 126 wordsVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
26
Examples that run
85%22 run in a plain check · 4 hold no code
Exported functions with examples
100%
Documented parameters
95%
Return-value docs
91%
Help pages citing references
46%

Downloads

3.6K
CRAN downloads in the past year
Rank #8,039 · ~10/day · ~303/mo
Daily download trend is not available in this view yet.
38930 days
1.1K90 days
3.6K1 year
Compare downloads with other packages →
Also on399 r2u

Repository

0Stars
0Forks
1Open issues
0Open PRs
0Releases
Last activity 2026-07-16 · Default branch at 1.0.8, ahead of CRAN 1.0.7 (checked 2026-10-18)
Responsiveness
not measured
PracticesBeta

1 development-tooling and community-health practice detected across 1 family in the upstream repository

Checks run against github.com/davidrusi/modelselection on 2026-10-18.

Lint, format, editor (1)
VS Code
How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies
15 external dependencies (excludes base and recommended)

Installing modelSelection adds 42 packages (24 with compiled code, 41 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, glmnet 2.7 MB. 1 more not on CRAN is not measured.

Depends (2)
R >= 2.14.0methods
Suggests (3)
Enhances (0)
none
Reverse dependencies
2direct
0indirect
Depends 1 · Imports 1
Depends, Imports or LinkingTo, most downloaded first

People & History

People (8)
Author, Maintainer since 1.0.3 (2025)
Contributor
Contributor
Contributor
Contributor
Listed in earlier versions (1)
Cook
no longer listed · 1.0.3 to 1.0.7
Releases (4)

Pick two releases to compare their code metrics.

  • 1.0.7Latest
    record
    2026-05-16 · current release · diff ↗ ·
    added 1 export
    modelSelectionGGM_eBayes
  • R
    R 4.6.0 released · 2026-04-24
  • 1.0.5
    record
    2026-01-19 · diff ↗
  • 1.0.4
    record
    2025-10-21 · diff ↗ ·
    added 4 exports
    bestAIC_fast, bestBIC_fast, bestEBIC_fast, bestIC_fast
  • 1.0.3
    record
    2025-09-21
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2025-09-21
Total releases
4 / 1 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 2.14.0
Download size
370 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("modelSelection")
Rossell, D., Abril, O., Cook, J. D., Hallahan, W., Mueller, P., Roebuck, P., Telesca, D., & Torrens, M. (2026). modelSelection: High-Dimensional Model Selection (Version 1.0.7) [Computer software]. https://doi.org/10.32614/CRAN.package.modelSelection

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for modelSelection version 1.0.7 [Data set]. HJJB, LLC. Data release v2026-10-20. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-20, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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