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:
stepPenal_0.2.tar.gz
stepPenal_0.2.zip(r-4.5)stepPenal_0.2.zip(r-4.4)stepPenal_0.2.zip(r-4.3)
stepPenal_0.2.tgz(r-4.4-any)stepPenal_0.2.tgz(r-4.3-any)
stepPenal_0.2.tar.gz(r-4.5-noble)stepPenal_0.2.tar.gz(r-4.4-noble)
stepPenal_0.2.tgz(r-4.4-emscripten)stepPenal_0.2.tgz(r-4.3-emscripten)
stepPenal.pdf |stepPenal.html✨
stepPenal/json (API)
# Install 'stepPenal' in R: |
install.packages('stepPenal', repos = c('https://veleni.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:7cc542f036. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 16 2024 |
R-4.5-win | NOTE | Nov 16 2024 |
R-4.5-linux | NOTE | Nov 16 2024 |
R-4.4-win | NOTE | Nov 16 2024 |
R-4.4-mac | NOTE | Nov 16 2024 |
R-4.3-win | OK | Nov 16 2024 |
R-4.3-mac | OK | Nov 16 2024 |
Exports:findROClassomodelobjFunoptimPenaLikoptimPenaLikL2penalBrierSimDatastepaicStepPenalStepPenalL2tuneParamtuneParamCL2
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tabledfoptimdiagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellmvtnormnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr