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ecpc
View on CRAN: Click
here
Download and install ecpc package within the R console
Install from CRAN:
install.packages("ecpc")
Install from Github:
library("remotes")
install_github("cran/ecpc") Install by package version:
library("remotes")
install_version("ecpc", "3.1.1") Attach the package and use:
library("ecpc")
Maintained by
Mirrelijn M. van Nee
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-05-03
Latest Update:
Description:
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) .
How to cite:
Mirrelijn M. van Nee (2021). ecpc: Flexible Co-Data Learning for High-Dimensional Prediction. R package version 3.1.1, https://cran.r-project.org/web/packages/ecpc. Accessed 27 Jun. 2026.
Previous versions and publish date:
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Complete documentation for ecpc
Functions, R codes and Examples using
the ecpc R package
Some associated functions: coef.ecpc . createCon . createGroupset . createS . createZforGroupset . createZforSplines . cv.ecpc . ecpc-package . ecpc . hierarchicalLasso . obtainHierarchy . plot.ecpc . postSelect . predict.ecpc . print.ecpc . produceFolds . simDat . splitMedian . visualiseGroupset . visualiseGroupsetweights . visualiseGroupweights .
Some associated R codes: ecpc.R . ecpc_methods.R . functions_create.R . Full ecpc package functions and examples
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