Fast Cross-Validation for Multi-Penalty Ridge Regression

Documentation for package ‘multiridge’ version 1.11

multiridge-package	Fast cross-validation for multi-penalty ridge regression
augment	Augment data with zeros.
betasout	Coefficient estimates from (converged) IWLS fit
createXblocks	Create list of paired data blocks
createXXblocks	Creates list of (unscaled) sample covariance matrices
CVfolds	Creates (repeated) cross-validation folds
CVscore	Cross-validated score
dataXXmirmeth	Contains R-object 'dataXXmirmeth'
doubleCV	Double cross-validation for estimating performance of 'multiridge'
fastCV2	Fast cross-validation per data block
IWLSCoxridge	Iterative weighted least squares algorithm for Cox ridge regression.
IWLSridge	Iterative weighted least squares algorithm for linear and logistic ridge regression.
mgcv_lambda	Maximum marginal likelihood score
mlikCV	Outer-loop cross-validation for estimating performance of marginal likelihood based 'multiridge'
multiridge	Fast cross-validation for multi-penalty ridge regression
optLambdas	Find optimal ridge penalties.
optLambdasWrap	Find optimal ridge penalties with sequential optimization.
optLambdas_mgcv	Find optimal ridge penalties with maximimum marginal likelihood
optLambdas_mgcvWrap	Find optimal ridge penalties with sequential optimization.
predictIWLS	Predictions from ridge fits
Scoring	Evaluate predictions
setupParallel	Setting up parallel computing
SigmaFromBlocks	Create penalized sample cross-product matrix