Package: stepPenal 0.2

stepPenal: Stepwise Forward Variable Selection in Penalized Regression

Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.

Authors:Eleni Vradi

stepPenal_0.2.tar.gz
stepPenal_0.2.zip(r-4.7-any)stepPenal_0.2.zip(r-4.6-any)stepPenal_0.2.zip(r-4.5-any)
stepPenal_0.2.tgz(r-4.6-any)stepPenal_0.2.tgz(r-4.5-any)
stepPenal_0.2.tar.gz(r-4.7-any)stepPenal_0.2.tar.gz(r-4.6-any)
stepPenal_0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
stepPenal/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.04 score 11 scripts 248 downloads 12 exports 77 dependencies

Last updated from:7cc542f036. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE118
source / vignettesOK178
linux-releaseNOTE124
macos-releaseNOTE201
macos-oldrelNOTE153
windows-develNOTE77
windows-releaseNOTE78
windows-oldrelNOTE74
wasm-releaseOK93

Exports:findROClassomodelobjFunoptimPenaLikoptimPenaLikL2penalBrierSimDatastepaicStepPenalStepPenalL2tuneParamtuneParamCL2

Dependencies:caretclasscliclockcodetoolscpp11data.tabledfoptimdiagramdigestdplyre1071farverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsmvtnormnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr