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multiridge

R package for multi-penalty ridge regression

library(devtools); install_github("markvdwiel/multiridge")

Demo script and data available from: https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4

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Version

Install

install.packages('multiridge')

Monthly Downloads

282

Version

1.11

License

GPL (>= 3)

Maintainer

Mark van de Wiel

Last Published

June 13th, 2022

Functions in multiridge (1.11)

Coefficient estimates from (converged) IWLS fit

Double cross-validation for estimating performance of multiridge

Contains R-object dataXXmirmeth

Fast cross-validation per data block

Maximum marginal likelihood score

optLambdas_mgcvWrap

Find optimal ridge penalties with sequential optimization.

optLambdas_mgcv

Find optimal ridge penalties with maximimum marginal likelihood

Outer-loop cross-validation for estimating performance of marginal likelihood based multiridge

Predictions from ridge fits

Setting up parallel computing

multiridge-package

Fast cross-validation for multi-penalty ridge regression

Find optimal ridge penalties.

Find optimal ridge penalties with sequential optimization.

Creates list of (unscaled) sample covariance matrices

Iterative weighted least squares algorithm for Cox ridge regression.

Evaluate predictions

Augment data with zeros.

Creates (repeated) cross-validation folds

Iterative weighted least squares algorithm for linear and logistic ridge regression.

Create list of paired data blocks

SigmaFromBlocks

Create penalized sample cross-product matrix

Cross-validated score