R: Fast Cross-Validation for Multi-Penalty Ridge Regression

# Fast Cross-Validation for Multi-Penalty Ridge Regression

## Documentation for package ‘multiridge’ version 1.11

- DESCRIPTION file
- Package README

## Help Pages

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