Package: msgps 1.3.5

Kei Hirose

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.tar.gz(r-4.5-noble)msgps_1.3.5.tar.gz(r-4.4-noble)
msgps_1.3.5.tgz(r-4.4-emscripten)msgps_1.3.5.tgz(r-4.3-emscripten)
msgps.pdf |msgps.html
msgps/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS

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

openblas

2.08 score 4 packages 8 scripts 402 downloads 1 mentions 13 exports 0 dependencies

Last updated 2 years agofrom:5f96b42596. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 01 2024
R-4.5-linux-x86_64OKDec 01 2024

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