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multiridge
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]
[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 22 Dec. 2024.
Previous versions and publish date:
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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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