Package: msgps 1.3.5

msgps: Degrees of Freedom of Elastic Net, Adaptive Lasso and Generalized Elastic Net

Computes the degrees of freedom of the lasso, elastic net, generalized elastic net and adaptive lasso based on the generalized path seeking algorithm. The optimal model can be selected by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC.

Authors:Kei Hirose

msgps_1.3.5.tar.gz
msgps_1.3.5.zip(r-4.7)msgps_1.3.5.zip(r-4.6)msgps_1.3.5.zip(r-4.5)
msgps_1.3.5.tgz(r-4.6-x86_64)msgps_1.3.5.tgz(r-4.6-arm64)msgps_1.3.5.tgz(r-4.5-x86_64)msgps_1.3.5.tgz(r-4.5-arm64)
msgps_1.3.5.tar.gz(r-4.7-arm64)msgps_1.3.5.tar.gz(r-4.7-x86_64)msgps_1.3.5.tar.gz(r-4.6-arm64)msgps_1.3.5.tar.gz(r-4.6-x86_64)
msgps_1.3.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
msgps/json (API)

# Install 'msgps' in R:
install.packages('msgps', repos = c('https://keihirose.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas

2.18 score 5 packages 8 scripts 290 downloads 1 mentions 13 exports 0 dependencies

Last updated from:5f96b42596. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK107
linux-devel-x86_64OK100
source / vignettesOK129
linux-release-arm64OK93
linux-release-x86_64OK97
macos-release-arm64OK199
macos-release-x86_64OK191
macos-oldrel-arm64OK168
macos-oldrel-x86_64OK544
windows-develOK73
windows-releaseOK141
windows-oldrelOK63
wasm-releaseOK83

Exports:aicc.dfgpsbic.dfgpscoef.dfgpscoef.msgpscoefmat.dfgpscp.dfgpsdfgpsgcv.dfgpsmsgpsplot.msgpspredict.msgpsprint.msgpssummary.msgps

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
msgps (Degrees of Freedom of Elastic Net, Adaptive Lasso and Generalized Elastic Net)aicc.dfgps bic.dfgps cp.dfgps dfgps gcv.dfgps msgps print.msgps
plot the solution path from a "msgps" object.plot.df plot.msgps
make predictions from a "msgps" object.coef.dfgps coef.msgps coef.step.dfgps coefmat.dfgps predict.msgps
A summary of "msgps" object..summary.msgps