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multiridge  

Fast Cross-Validation for Multi-Penalty Ridge Regression
View on CRAN: Click here


Download and install multiridge package within the R console
Install from CRAN:
install.packages("multiridge")

Install from Github:
library("remotes")
install_github("cran/multiridge")

Install by package version:
library("remotes")
install_version("multiridge", "1.11")



Attach the package and use:
library("multiridge")
Maintained by
Mark A. van de Wiel
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-04-06
Latest Update: 2022-06-13
Description:
Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), .
How to cite:
Mark A. van de Wiel (2021). multiridge: Fast Cross-Validation for Multi-Penalty Ridge Regression. R package version 1.11, https://cran.r-project.org/web/packages/multiridge. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.5 (2021-04-06 09:50), 1.7 (2021-04-28 11:40), 1.9 (2021-06-15 09:40)
Other packages that cited multiridge R package
View multiridge citation profile
Other R packages that multiridge depends, imports, suggests or enhances
Complete documentation for multiridge
Functions, R codes and Examples using the multiridge R package
Some associated functions: CVfolds . CVscore . IWLSCoxridge . IWLSridge . Scoring . SigmaFromBlocks . augment . betasout . createXXblocks . createXblocks . dataXXmirmeth . doubleCV . fastCV2 . mgcv_lambda . mlikCV . multiridge-package . optLambdas . optLambdasWrap . optLambdas_mgcv . optLambdas_mgcvWrap . predictIWLS . setupParallel . 
Some associated R codes: MultiLambdaCVfun.R .  Full multiridge package functions and examples
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