egg, ggplot2, gtools, igraph, MASS, methods

Bayesian Estimation and Optimisation of Expected False Discovery
Rate
c(
person("Alan", "O'Callaghan",
email = "alan.ocallaghan@outlook.com",
role = c("aut", "cre")
),
person("Catalina", "Vallejos", role = "aut")
)
Implements the Bayesian FDR control described by
Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>.
Allows optimisation and visualisation of expected error rates based on
tail posterior probability tests.
Based on code written by Catalina Vallejos for BASiCS, see
Beyond comparisons of means: understanding changes in gene expression at the
single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.
ggplot2, reshape2, assertthat, utils, stats
Alan O'Callaghan [aut, cre],
Catalina Vallejos [aut]

ggplot2, reshape2, assertthat, utils, cowplot, ggExtra, stats

User Friendly Bayesian Data Analysis for Psychology
Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics.
c(person("Jure", "Demšar", , "jure.demsar@fri.uni-lj.si", c("cre", "aut")),
person("Grega", "Repovš", , "grega.repovs@psy.ff.uni-lj.si", "aut"),
person("Erik", "Štrumbelj", , "erik.strumbelj@fri.uni-lj.si", "aut"),
person("Trustees of", "Columbia University", role = "cph"),
person("John", "Kruschke", role = "cph", comment = "R/shared_functions.R - mcmc_hdi, src/stan_files/ttest.stan"),
person("Rasmus", "Baath", role = "cph", comment = "R/b_bootstrap.R"))
Jure Demšar [cre, aut],
Grega Repovš [aut],
Erik Štrumbelj [aut],
Trustees of Columbia University [cph],
John Kruschke [cph] (R/shared_functions.R - mcmc_hdi,
src/stan_files/ttest.stan),
Rasmus Baath [cph] (R/b_bootstrap.R)

Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).