This package is considered a duplicate. The official version of this package is found at:https://veleni.r-universe.dev/stepPenal

Package: stepPenal 0.2

Eleni Vradi

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://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

On CRAN:

archived packages r-universe

1.74 score 5 stars 11 scripts 242 downloads 12 exports 77 dependencies

Last updated from:8327df0d4e (on package/stepPenal). Checks:7 NOTE, 2 OK. Indexed: no.

Target	Result	Time
linux-devel	NOTE	120
source / vignettes	OK	228
linux-release	NOTE	113
macos-release	NOTE	117
macos-oldrel	NOTE	86
windows-devel	NOTE	94
windows-release	NOTE	137
windows-oldrel	NOTE	79
wasm-release	OK	108

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

Dependencies:caret class cli clock codetools cpp11 data.table dfoptim diagram digest dplyr e1071 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 ModelMetrics mvtnorm nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp RcppEigen recipes reshape2 rlang rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

Citation

Development and contributors

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