Package: BranchGLM 3.0.1

Jacob Seedorff

BranchGLM: Efficient Best Subset Selection for GLMs via Branch and Bound Algorithms

Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'.

Authors:Jacob Seedorff [aut, cre]

BranchGLM_3.0.1.tar.gz
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BranchGLM_3.0.1.tgz(r-4.6-x86_64)BranchGLM_3.0.1.tgz(r-4.6-arm64)BranchGLM_3.0.1.tgz(r-4.5-x86_64)BranchGLM_3.0.1.tgz(r-4.5-arm64)
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BranchGLM_3.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
BranchGLM/json (API)

# Install 'BranchGLM' in R:
install.packages('BranchGLM', repos = c('https://jacobseedorff21.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jacobseedorff21/branchglm/issues

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

On CRAN:

Conda:

generalized-linear-modelsregressionstatisticssubset-selectionvariable-selectionopenblascppopenmp

5.60 score 8 stars 33 scripts 369 downloads 12 exports 3 dependencies

Last updated from:4f8a052d0a. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK232
linux-devel-x86_64OK209
source / vignettesOK318
linux-release-arm64OK244
linux-release-x86_64OK254
macos-release-arm64OK203
macos-release-x86_64OK322
macos-oldrel-arm64OK163
macos-oldrel-x86_64OK334
windows-develOK244
windows-releaseOK257
windows-oldrelOK225
wasm-releaseOK186

Exports:AUCBranchGLMBranchGLM.fitCindexfit.BranchGLMVSMultipleROCCurvesplotCIROCTableVariableImportanceVariableImportance.bootVariableSelection

Dependencies:BHRcppRcppArmadillo

Variable Importance Vignette
Variable Importance | Definition | Variable importance example | P-values
Rendered fromVariableImportance-Vignette.Rmdusingknitr::rmarkdown

Last update: 2024-08-20
Started: 2024-08-20

VariableSelection Vignette
Performing variable selection | Metrics | Stepwise algorithms | Forward selection | Backward elimination | Traditional variant | Fast variant | Double backward elimination | Branch and bound | Branch and bound example | Using bestmodels | Using cutoff | Using keep | Categorical variables | Convergence issues
Rendered fromVariableSelection-Vignette.Rmdusingknitr::rmarkdown

Last update: 2024-08-20
Started: 2022-10-31

BranchGLM Vignette
Fitting GLMs | Optimization methods | Initial values | Parallel computation | Families | Gaussian | Gamma | Poisson | Binomial | Functions for binomial GLMs | Table | ROC | Cindex/AUC | MultipleROCPlots | Using predictions | Useful functions
Rendered fromBranchGLM-Vignette.Rmdusingknitr::rmarkdown

Last update: 2023-12-06
Started: 2022-05-07

Readme and manuals

Help Manual

Help pageTopics
Bar Plot Method for BranchGLMVI Objectsbarplot.BranchGLMVI
Box Plot Method for BranchGLMVI.boot Objectsboxplot.BranchGLMVI.boot
Fits GLMsBranchGLM BranchGLM.fit
Cindex/AUCAUC Cindex Cindex.BranchGLM Cindex.BranchGLMROC Cindex.numeric
Extract Coefficients from BranchGLM Objectscoef.BranchGLM
Extract Coefficients from BranchGLMVS or summary.BranchGLMVS Objectscoef.BranchGLMVS coef.summary.BranchGLMVS
Likelihood Ratio Confidence Intervals for Beta Coefficients for BranchGLM Objectsconfint.BranchGLM
Extract the Deviancedeviance.BranchGLM
Extract AIC from BranchGLM ObjectsextractAIC.BranchGLM
Extract Family from BranchGLM Objectsfamily.BranchGLM
Extract Model Formula from BranchGLM Objectsformula.BranchGLM
Histogram Method for BranchGLMVI.boot Objectshist.BranchGLMVI.boot
Extract Log-Likelihood from BranchGLM ObjectslogLik.BranchGLM
Extract Model Frame from a BranchGLM Objectmodel.frame.BranchGLM
Plotting Multiple ROC CurvesMultipleROCCurves
Extract Number of Observations from BranchGLM Objectsnobs.BranchGLM
Plot Method for BranchGLM Objectsplot.BranchGLM
Plot Method for BranchGLMCIs Objectsplot.BranchGLMCIs
Plot Method for BranchGLMROC Objectsplot.BranchGLMROC
Plot Method for summary.BranchGLMVS and BranchGLMVS Objectsplot.BranchGLMVS plot.summary.BranchGLMVS
Plot Confidence IntervalsplotCI
Predict Method for BranchGLM Objectspredict.BranchGLM
Predict Method for BranchGLMVS or summary.BranchGLMVS Objectspredict.BranchGLMVS predict.summary.BranchGLMVS
Print Method for BranchGLM Objectsprint.BranchGLM
Print Method for BranchGLMCIs Objectsprint.BranchGLMCIs
Print Method for BranchGLMROC Objectsprint.BranchGLMROC
Print Method for BranchGLMTable Objectsprint.BranchGLMTable
Print Method for BranchGLMVI Objectsprint.BranchGLMVI
Print Method for BranchGLMVI.boot Objectsprint.BranchGLMVI.boot
Print Method for BranchGLMVS Objectsprint.BranchGLMVS
Print Method for summary.BranchGLMVS Objectsprint.summary.BranchGLMVS
Extract the Pearson Residuals from BranchGLM Objectsresiduals.BranchGLM
ROC CurveROC ROC.BranchGLM ROC.numeric
Extract Square Root of the Dispersion Parameter Estimatessigma.BranchGLM
Summary Method for BranchGLMVS Objectssummary.BranchGLMVS
Confusion MatrixTable Table.BranchGLM Table.numeric
Computes Exact or Approximate L0-penalization based Variable Importance for GLMsVariableImportance
Performs Parametric Bootstrap for Modified Variable ImportanceVariableImportance.boot VariableImportance.boot.BranchGLMVI VariableImportance.boot.BranchGLMVS
Variable Selection for GLMsVariableSelection VariableSelection.BranchGLM VariableSelection.formula
Extract covariance matrix from BranchGLM Objectsvcov.BranchGLM