Designing Bayesian Multi-Arm Multi-Stage Studies
Calculating Bayesian sample sizes for multi-arm trials where several experimental treatments are compared to a common control, perhaps even at multiple stages.

c(person(given = "Minnie M.",
family = "Joo",
role = c("aut")),
person(given = "Sergio",
family = "Bejar",
role = c("aut")),
person(given = "Nicolas",
family = "Schmidt",
role = c("aut", "cre"),
email = "nschmidt@cienciassociales.edu.uy"),
person(given = "Bumba",
family = "Mukherjee",
role = c("aut"))
)
Contains a split population survival estimator that models the
misclassification probability of failure versus right-censored events.
The split population survival estimator is described in
Bagozzi et al. (2019) <doi:10.1017/pan.2019.6>.
MCMCpack, FastGP, stats, Rcpp (>= 1.0.3), coda, mvtnorm,
Minnie M. Joo [aut],
Sergio Bejar [aut],
Nicolas Schmidt [aut, cre],
Bumba Mukherjee [aut]

Bayesian Multi-Resolution Gaussian Process Approximations
person("John", "Tipton", email = "jrtipton@uark.edu",
role = c("aut", "cre"))
Software for fitting sparse Bayesian multi-resolution spatial models using Markov Chain Monte Carlo.
fields, igraph, Matrix, mvnfast, Rcpp (>= 1.0.4.6), spam
knitr, pkgdown, rmarkdown, testthat (>= 2.1.0), covr

Bayesian Preference Learning with the Mallows Rank Model
Oystein Sorensen, Valeria Vitelli, Marta Crispino, Qingua Liu
An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <http://jmlr.org/papers/v18/15-481.html>). Both Cayley, footrule, Kendall, and Spearman distances are supported in the models. The rank data to be analyzed can be the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
testthat, gtools (>= 3.8.1), knitr, PerMallows (>= 1.13),
rmarkdown, Rankcluster (>= 0.94)