Implements various procedures for finding
multiple change-points. Two methods make use of dynamic
programming and pruning, with no distributional
assumptions other than the existence of certain absolute
moments in one method. Hierarchical and exact search methods
are included. All methods return the set of estimated change-
points as well as other summary information.

Implements various procedures for finding
multiple change-points from Matteson D. et al (2013)
<doi:10.1080/01621459.2013.849605>, Zhang W. et al (2017)
<doi:10.1109/ICDMW.2017.44>, Arlot S. et al (2019).
Two methods make use of dynamic
programming and pruning, with no distributional
assumptions other than the existence of certain absolute
moments in one method. Hierarchical and exact search methods
are included. All methods return the set of estimated change-
points as well as other summary information.

Flexible Co-Data Learning for High-Dimensional Prediction
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"))
Mirrelijn M. van Nee [aut, cre],
Lodewyk F.A. Wessels [aut],
Mark A. van de Wiel [aut]
Mirrelijn M. van Nee <m.vannee@amsterdamumc.nl>
glmnet, stats, Matrix, gglasso, mvtnorm, CVXR, multiridge (>=
1.5), survival, pROC
Rsolnp, expm, mgcv, foreach, doParallel, parallel, ggplot2,
ggraph, igraph, scales, dplyr, magrittr
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.
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>.