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-4.7-any)ProSGPV_1.0.1.zip(r-4.6-any)ProSGPV_1.0.1.zip(r-4.5-any)
ProSGPV_1.0.1.tgz(r-4.6-any)ProSGPV_1.0.1.tgz(r-4.5-any)
ProSGPV_1.0.1.tar.gz(r-4.7-any)ProSGPV_1.0.1.tar.gz(r-4.6-any)
ProSGPV_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ProSGPV/json (API)

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

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

Datasets:

On CRAN:

Conda:

penalized-regression

4.74 score 5 stars 11 scripts 340 downloads 2 exports 17 dependencies

Last updated from:d07b8f77d2. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK126
source / vignettesOK197
linux-releaseOK164
macos-releaseOK232
macos-oldrelOK216
windows-develOK96
windows-releaseOK88
windows-oldrelOK111
wasm-releaseOK91

Exports:gen.sim.datapro.sgpv

Dependencies:brglm2codetoolsenrichwithforeachglmnetiteratorslatticeMASSMatrixnleqslvnnetnumDerivRcppRcppEigenshapestatmodsurvival

ProSGPV in GLM and Cox models
Introduction | GLM examples | Logistic regression | Poisson regression | Cox regression | Way to address high correlation or dense signals | References

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

ProSGPV in linear regression
Introduction | Two-stage algorithm | One-stage algorithm | High dimensional case where $p>n$ | Way to address high correlation or dense signals | References

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