ecpc 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 2022