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
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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'))

On CRAN:

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 100 downloads 12 exports 79 dependencies

Last updated 6 years agofrom:7cc542f036. Checks:OK: 3 NOTE: 4. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 17 2024
R-4.5-win	NOTE	Oct 17 2024
R-4.5-linux	NOTE	Oct 17 2024
R-4.4-win	NOTE	Oct 17 2024
R-4.4-mac	NOTE	Oct 17 2024
R-4.3-win	OK	Oct 17 2024
R-4.3-mac	OK	Oct 17 2024

Exports:findROC lassomodel objFun optimPenaLik optimPenaLikL2 penalBrier SimData stepaic StepPenal StepPenalL2 tuneParam tuneParamCL2

Dependencies:caret class cli clock codetools colorspace cpp11 data.table dfoptim diagram digest dplyr e1071 fansi farver foreach future future.apply generics ggplot2 glmnet globals glue gower gtable hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix mgcv ModelMetrics munsell mvtnorm nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp RcppEigen recipes reshape2 rlang rpart scales shape SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

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Readme and manuals

Help Manual

Help page	Topics
Compute the area under the ROC curve	findROC
Fits a lasso model and a lasso followed by a stepAIC algorithm.	lassomodel
Objective function	objFun
Variable selection based on the combined penalty CL= (1-w)L0 + wL1	optimPenaLik
Variable selection based on the combined penalty CL2= (1-w)L0 + wL2	optimPenaLikL2
Evaluation of the performance of risk prediction models with binary status response variable.	penalBrier
Simulate data with normally distributed predictors and binary response	SimData
Stepwise forward variable selection based on the AIC criterion	stepaic
Stepwise forward variable selection using penalized regression.	StepPenal
Stepwise forward variable selection using penalized regression.	StepPenalL2
Tune parameters w and lamda using the CL penalty	tuneParam
Tune parameters w and lamda using the CL2 penalty	tuneParamCL2

Package: stepPenal 0.2

stepPenal: Stepwise Forward Variable Selection in Penalized Regression

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)