swMATH ID: 42221
Software Authors: Mirrelijn M. van Nee, Lodewyk F.A. Wessels, Mark A. van de Wiel
Description: R package ecpc: Flexible Co-Data Learning for High-Dimensional Prediction. Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties. The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources. Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage. Various types of hyper shrinkage may be used for various co-data. Co-data may be continuous or categorical. The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types. The model fit is used to predict for new samples. The name ’ecpc’ stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection. See Van Nee et al. (2020) <arXiv:2005.04010>.
Homepage: https://cran.r-project.org/web/packages/ecpc/index.html
Source Code:  https://github.com/cran/ecpc
Dependencies: R
Keywords: R package; R; ecpc; arXiv_stat.ME; Machine Learning; arXiv_stat.ML; High-dimensional prediction
Related Software: gren; CoRF; glmnet; scam; mgcv; fwelnet; graper; GRridge; gglasso; grplasso; ggplot2; ggpubr; squeezy; R
Cited in: 0 Publications

Standard Articles

1 Publication describing the Software Year
ecpc: An R-package for generic co-data models for high-dimensional prediction
Mirrelijn M. van Nee, Lodewyk F.A. Wessels, Mark A. van de Wiel