Introduction | Model | BIC | Example | Data | Formula | Interactions | Stochastic search | Model fit | GLM | Functions | Reference
Package: VariableSelection 1.0.0
VariableSelection: Select Variables for Linear Models
Provides variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) <doi:10.48550/arXiv.2510.02628>.
Authors:
VariableSelection_1.0.0.tar.gz
VariableSelection_1.0.0.zip(r-4.7)VariableSelection_1.0.0.zip(r-4.6)VariableSelection_1.0.0.zip(r-4.5)
VariableSelection_1.0.0.tgz(r-4.6-any)VariableSelection_1.0.0.tgz(r-4.5-any)
VariableSelection_1.0.0.tar.gz(r-4.7-any)VariableSelection_1.0.0.tar.gz(r-4.6-any)
VariableSelection_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
VariableSelection/json (API)
| # Install 'VariableSelection' in R: |
| install.packages('VariableSelection', repos = c('https://xss55.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/xss55/variableselection/issues
Datasets:
Last updated from:ed284fd6e4. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 124 | ||
| source / vignettes | OK | 177 | ||
| linux-release-x86_64 | OK | 120 | ||
| macos-release-arm64 | OK | 80 | ||
| macos-oldrel-arm64 | OK | 87 | ||
| windows-devel | OK | 67 | ||
| windows-release | OK | 69 | ||
| windows-oldrel | OK | 73 | ||
| wasm-release | OK | 103 |
Exports:glm.bestlm.bestmodelselect.glmmodelselect.lm
Dependencies:cachemclicodetoolscrayonfastmapforeachGAiteratorsmemoiseRcppRcppArmadillorlang
