Imports: e1071, rpart, C50, randomForest, pROC, smotefamily

Title: Empirical Bayes Multi-State Cox Model
Description: Implements an empirical Bayes, multi-state Cox model for survival analysis. Run "?'ebmstate-package'" for details. See also Schall (1991) <doi:10.1093/biomet/78.4.719>.
Imports: Rcpp, HDInterval, stats, utils, methods
Packaged: 2020-08-11 17:53:17 UTC; ruibarrigana
Terry M Therneau [ctb] (author of 'survival', a package from which code
Thomas Lumley [ctb] (contributor to 'survival', a package from which
Hein Putter [ctb] (co-author of 'mstate', a package from which code
Liesbeth de Wreede [ctb] (co-author of 'mstate', a package from which
Marta Fiocco [ctb] (co-author of 'mstate', a package from which code
Ronald Geskus [ctb] (contributor to 'mstate', a package from which code

Packaged: 2020-08-24 16:22:19 UTC; ruibarrigana

Packaged: 2021-01-05 16:48:15 UTC; ruibarrigana

Packaged: 2021-12-20 15:01:38 UTC; ruibarrigana

Title: Solve the Empirical Bayes Normal Means Problem
Authors@R: c(person("Willwerscheid", "Jason", role = "aut"),
person("Stephens", "Matthew", role = "aut"),
person("Carbonetto", "Peter", role = c("aut", "cre"),
person("Goldstein", "Andrew", role = "ctb"))
Description: Provides simple, fast, and stable functions to fit the normal
means model using empirical Bayes. For available models and details, see
function ebnm(). A comprehensive introduction to the package is provided
by Willwerscheid and Stephens (2021) <arXiv:2110.00152>.
Imports: stats, methods, ashr, mixsqp, truncnorm, trust, horseshoe,
deconvolveR, magrittr, rlang, dplyr, ggplot2

Title: Implementation of the Empirical Bayes Method
Description: Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) <doi:10.3150/15-BEJ797>. More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) <arXiv:1903.00961>.