Package: modelSelection 1.0.7

modelSelection: High-Dimensional Model Selection

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.

Authors:David Rossell [aut, cre], John D. Cook [ctb], Donatello Telesca [aut], P. Roebuck [ctb], Oriol Abril [aut], Miquel Torrens [aut], Peter Mueller [ctb], William Hallahan [ctb]

modelSelection_1.0.7.tar.gz
modelSelection_1.0.7.zip(r-4.7)modelSelection_1.0.7.zip(r-4.6)modelSelection_1.0.7.zip(r-4.5)
modelSelection_1.0.7.tgz(r-4.6-x86_64)modelSelection_1.0.7.tgz(r-4.6-arm64)modelSelection_1.0.7.tgz(r-4.5-x86_64)modelSelection_1.0.7.tgz(r-4.5-arm64)
modelSelection_1.0.7.tar.gz(r-4.7-arm64)modelSelection_1.0.7.tar.gz(r-4.7-x86_64)modelSelection_1.0.7.tar.gz(r-4.6-arm64)modelSelection_1.0.7.tar.gz(r-4.6-x86_64)
modelSelection_1.0.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
modelSelection/json (API)

# Install 'modelSelection' in R:
install.packages('modelSelection', repos = c('https://davidrusi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/davidrusi/modelselection/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.88 score 15 scripts 163 downloads 69 exports 52 dependencies

Last updated from:ce4f321b68. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK252
linux-devel-x86_64OK268
source / vignettesOK263
linux-release-arm64OK256
linux-release-x86_64OK268
macos-release-arm64OK142
macos-release-x86_64OK359
macos-oldrel-arm64OK180
macos-oldrel-x86_64OK505
windows-develOK284
windows-releaseOK271
windows-oldrelOK309
wasm-releaseOK184

Exports:aicbbPriorbestAICbestAIC_fastbestBICbestBIC_fastbestEBICbestEBIC_fastbestICbestIC_fastbfnormmixbicbinomPriorcilcoefByModeldalaplddirdemomdemomigmargdimomdiwishdmomdmomigmargdpostNIWemomprioreprodexponentialpriorgroupemompriorgroupimompriorgroupmompriorgroupzellnerprioricicarplusprioricovigpriorimompriorlocalnulltestlocalnulltest_fdalocalnulltest_fda_givenknotslocalnulltest_givenknotsmarginalLikelihoodmarginalNIWmodelbbpriormodelbinompriormodelcomplexpriormodelSelectionmodelSelection_eBayesmodelSelectionGGMmodelSelectionGGM_eBayesmodelunifpriormompriornormalidpriorpalaplpemompemomigmargpimomplotpriorpmompmomigmargpostProbpostSamplespriorp2gqimomqmomralaplrnlprpostNIWunifPriorzellnerprior

Dependencies:clicodetoolscpp11dplyrfarverforeachgenericsggplot2glmnetgluegtableintervalsisobanditeratorsL0LearnlabelinglatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmclustmgcvmvtnormncvregnlmepillarpkgconfigplyrpracmaR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2rlangS7scalesshapesparseMatrixStatsstringistringrsurvivaltibbletidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Priors on model space for variable selection problemsbbPrior binomPrior unifPrior
Model with best AIC, BIC, EBIC or other general information criteria (getIC)bestAIC bestAIC_fast bestBIC bestBIC_fast bestEBIC bestEBIC_fast bestIC bestIC_fast
Number of Normal mixture components under Normal-IW and Non-local priorsbfnormmix
Treatment effect estimation for linear models via Confounder Importance Learning using non-local priors.cil
Density and random draws from the asymmetric Laplace distributiondalapl palapl ralapl
Dirichlet densityddir
Density for Inverse Wishart distributiondiwish
Non-local prior density, cdf and quantile functions.demom demom,data.frame-method demom,matrix-method demom,vector-method demom-methods demomigmarg dimom dmom dmomigmarg pemom pemomigmarg pimom pmom pmomigmarg qimom qmom
Posterior Normal-IWishart densitydpostNIW rpostNIW
Expectation of a product of powers of Normal or T random variableseprod
Class "icfit"icfit icfit-class icfit.coef icfit.predict icfit.summary show,icfit-method
Extract estimated inverse covarianceicov
Local variable selectionlocalnulltest localnulltest_fda localnulltest_fda_givenknots localnulltest_givenknots
Marginal (or integrated) likelihood density of the observed data for an individual model handled by modelSelection (regression, GLM, GAM, accelerated failure time, regression with Normal, two-piece Normal, Laplace or two-piece Laplace residualsmarginalLikelihood
Marginal likelihood under a multivariate Normal likelihood and a conjugate Normal-inverse Wishart prior.marginalNIW marginalNIW,matrix,missing,missing,missing,missing-method marginalNIW,matrix,missing,missing,missing,vector-method marginalNIW,missing,ANY,matrix,numeric,missing-method marginalNIW,missing,list,list,numeric,missing-method marginalNIW-methods
Class "mixturebf"coef.mixturebf mixturebf mixturebf-class show,mixturebf-method
Bayesian variable selection for generalized linear and generalized additive models.modelsearchBlockDiag modelSelection modelSelection_eBayes
Bayesian variable selection for Gaussian graphical modelsmodelSelectionGGM modelSelectionGGM_eBayes
Class "msfit_ggm"msfit_ggm msfit_ggm-class msfit_ggm.coef show,msfit_ggm-method
Class "msfit"coefByModel coefByModel,msfit-method coefByModel-methods msfit msfit-class msfit.coef msfit.plot msfit.predict show,msfit-method
Class "msPriorSpec"aic bic bicprior emomprior exponentialprior groupemomprior groupimomprior groupmomprior groupzellnerprior ic icarplusprior igprior imomprior modelbbprior modelbinomprior modelcomplexprior modelunifprior momprior msPriorSpec msPriorSpec-class normalidprior zellnerprior
Plot estimated marginal prior inclusion probabilitiesplotprior plotprior,cilfit-method plotprior-methods
Obtain posterior model probabilitiespostProb postProb,cilfit-method postProb,localtest-method postProb,mixturebf-method postProb,msfit-method postProb,msfit_ggm-method postProb-methods
Extract posterior samples from an objectpostSamples postSamples,mixturebf-method postSamples-methods
Moment and inverse moment prior elicitationpriorp2g
Posterior sampling for regression parametersrnlp rnlp,ANY,matrix,missing,missing,missing,character,character-method rnlp,ANY,matrix,missing,missing,missing,missing,missing-method rnlp,ANY,matrix,missing,missing,msfit,missing,missing-method rnlp,missing,missing,missing,missing,msfit,missing,missing-method rnlp,missing,missing,numeric,matrix,missing,missing,missing-method rnlp-methods