Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data, and for medians [Fay and Brittain <doi:10.1002/sim.6905>]. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain <doi:10.1111/biom.12231>]. Especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options.

Bayesian Paired Comparison Analysis with Stan
c(
person(given = "David",
family = "Issa Mattos",
role = c("aut", "cre"),
email = "issamattos.david@gmail.com",
comment = c(ORCID = "0000-0002-2501-9926")),
person(given="Erika",
family="Martins Silva Ramos",
role = c("aut"),
email = "erikamsramos@gmail.com",
comment = c(ORCID = "0000-0001-7393-1410"))
)
Models for the analysis of paired comparison data using Stan. The models include Bayesian versions of the Bradley-Terry model, including random effects (1 level), generalized model for predictors, order effect (home advantage) and the  variations for the Davidson (1970) model to handle ties. Additionally, we provide a number of functions to facilitate inference and obtaining results with these models. References: Bradley and Terry (1952) <doi:10.2307/2334029>; Davidson (1970) <doi:10.1080/01621459.1970.10481082>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
coda, dplyr, tidyr, stringr, ggplot2, gtools, methods, Rcpp
(>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.20.0),
rstantools (>= 2.1.0), tibble, tidyselect, HDInterval,
shinystan, loo, magrittr, badger, stats, rlang, knitr
rmarkdown, testthat, covr, bayesplot, kableExtra
David Issa Mattos [aut, cre] (<https://orcid.org/0000-0002-2501-9926>),
Erika Martins Silva Ramos [aut]
(<https://orcid.org/0000-0001-7393-1410>)

Bayesian Model Selection Approach for Parsimonious Gaussian
Mixture Models
Xiang Lu <Xiang_Lu at urmc.rochester.edu>,
Yaoxiang Li <yl814 at georgetown.edu>,
Tanzy Love <tanzy_love at urmc.rochester.edu>
Model-based clustering using Bayesian parsimonious Gaussian mixture models.
MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection.
GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.