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msgps
View on CRAN: Click
here
Download and install msgps package within the R console
Install from CRAN:
install.packages("msgps")
Install from Github:
library("remotes")
install_github("cran/msgps") Install by package version:
library("remotes")
install_version("msgps", "1.3.5") Attach the package and use:
library("msgps")
Maintained by
Kei Hirose
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2011-12-01
Latest Update: 2022-10-20
Description:
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.
How to cite:
Kei Hirose (2011). msgps: Degrees of Freedom of Elastic Net, Adaptive Lasso and Generalized Elastic Net. R package version 1.3.5, https://cran.r-project.org/web/packages/msgps. Accessed 06 Jun. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for msgps
Functions, R codes and Examples using
the msgps R package
Some associated functions: msgps . plot.msgps . predict.msgps . summary.msgps .
Some associated R codes: aicc.dfgps.R . bic.dfgps.R . coef.dfgps.R . coef.msgps.R . coef.step.dfgps.R . coefmat.dfgps.R . cp.dfgps.R . dfgps.R . gcv.dfgps.R . msgps.R . plot.df.R . plot.msgps.R . predict.msgps.R . print.msgps.R . summary.msgps.R . Full msgps package functions and examples
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