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msgps  

Degrees of Freedom of Elastic Net, Adaptive Lasso and Generalized Elastic Net
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]
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 12 Apr. 2025.
Previous versions and publish date:
1.0 (2011-12-01 11:40), 1.1 (2011-12-06 18:09), 1.3.1 (2019-01-27 12:55), 1.3.4 (2022-05-10 09:10), 1.3 (2012-05-17 08:51)
Other packages that cited msgps R package
View msgps citation profile
Other R packages that msgps depends, 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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