Package: ecpc
Type: Package
Title: Flexible Co-Data Learning for High-Dimensional Prediction
Version: 3.1
Date: 2022-05-13
Authors@R: c(person(c("Mirrelijn","M."), "van Nee", role = c("aut", "cre"),
email = "m.vannee@amsterdamumc.nl"),
person(c("Lodewyk","F.A."), "Wessels", role = "aut"),
person(c("Mark","A."), "van de Wiel", role = "aut"))
Author: Mirrelijn M. van Nee [aut, cre],
Lodewyk F.A. Wessels [aut],
Mark A. van de Wiel [aut]
Maintainer: Mirrelijn M. van Nee <m.vannee@amsterdamumc.nl>
Depends: R (>= 3.5.0)
Imports: glmnet, stats, Matrix, gglasso, mvtnorm, CVXR, multiridge (>=
1.5), survival, pROC, mgcv, pracma, JOPS, quadprog, checkmate
Suggests: Rsolnp, expm, foreach, doParallel, parallel, ggplot2, ggraph,
igraph, ggpubr, scales, dplyr, magrittr, nnls
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) <arXiv:2005.04010>.
License: GPL (>= 3)
URL: http://dx.doi.org/10.1002/sim.9162
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2022-05-13 12:54:26 UTC; VNOB-0728
Repository: CRAN
Date/Publication: 2022-05-13 14:30:05 UTC

Description: Provides a powerful framework for evolutionary
computing in R. The user can easily construct powerful evolutionary algorithms
for tackling both single- and multi-objective problems by plugging in different
predefined evolutionary building blocks, e. g., operators for mutation,
recombination and selection with just a few lines of code. Your problem
cannot be easily solved with a standard EA which works on real-valued vectors,
permutations or binary strings? No problem, 'ecr' has been developed with that in
mind. Extending the framework with own operators is also possible. Additionally
there are various comfort functions, like monitoring, logging and more.
c("aut", "cre")), person("Dirk", "Surmann", email = "surmann@statistik.tu-dortmund.de", role = "ctb"))
Depends: ParamHelpers (>= 1.8), BBmisc (>= 1.6), smoof (>= 1.4),
Imports: checkmate (>= 1.1), parallelMap (>= 1.1), reshape2 (>= 1.4.1),