Package: ProSGPV 1.0.1

ProSGPV: Penalized Regression with Second-Generation P-Values

Implementation of penalized regression with second-generation p-values for variable selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details can be found at Zuo et al. (2021) <doi:10.1080/00031305.2021.1946150>.

Authors:Yi Zuo [aut, cre], Thomas Stewart [aut], Jeffrey Blume [aut]

ProSGPV_1.0.1.tar.gz
ProSGPV_1.0.1.zip(r-4.5)ProSGPV_1.0.1.zip(r-4.4)ProSGPV_1.0.1.zip(r-4.3)
ProSGPV_1.0.1.tgz(r-4.4-any)ProSGPV_1.0.1.tgz(r-4.3-any)
ProSGPV_1.0.1.tar.gz(r-4.5-noble)ProSGPV_1.0.1.tar.gz(r-4.4-noble)
ProSGPV_1.0.1.tgz(r-4.4-emscripten)ProSGPV_1.0.1.tgz(r-4.3-emscripten)
ProSGPV.pdf |ProSGPV.html✨
ProSGPV/json (API)
NEWS

# Install 'ProSGPV' in R:
install.packages('ProSGPV', repos = c('https://zuoyi93.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/zuoyi93/prosgpv/issues

Datasets:

On CRAN:

penalized-regression

4.70 score 5 stars 9 scripts 158 downloads 2 exports 15 dependencies

Last updated 3 years agofrom:d07b8f77d2. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winOKOct 27 2024
R-4.5-linuxOKOct 27 2024
R-4.4-winOKOct 27 2024
R-4.4-macOKOct 27 2024
R-4.3-winOKOct 27 2024
R-4.3-macOKOct 27 2024

Exports:gen.sim.datapro.sgpv

Dependencies:brglm2codetoolsenrichwithforeachglmnetiteratorslatticeMASSMatrixnnetnumDerivRcppRcppEigenshapesurvival

ProSGPV in GLM and Cox models

Rendered fromglm-cox-vignette.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2021-08-24
Started: 2021-04-22

ProSGPV in linear regression

Rendered fromlinear-vignette.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2021-08-24
Started: 2021-03-28