person("Frank", "Portman", email = "frank1214@gmail.com", role = c("aut", "cre"))
A suite of functions that allow the user to analyze A/B test
data in a Bayesian framework. Intended to be a drop-in replacement for
common frequentist hypothesis test such as the t-test and chi-sq test.
Rcpp (>= 0.12.4), ggplot2, reshape2, methods

Rcpp (>= 0.12.4), ggplot2, reshape2, methods, plumber,
devtools

testthat, knitr, rmarkdown, magrittr, plumber (>= 0.3.0)

A set of methods for Bayesian analysis of proportions of chromosomal changes within clinical sets.

Bayesian Inference on a GPU using OpenCL
Bayesian Inference on a GPU. The package currently supports sampling from PolyaGamma, Multinomial logit and Bayesian lasso.
OpenCL library; single-precision AMD or Nvidia GPU;

Simulation and Analysis of Adaptive Bayesian Clinical Trials
c(person(given = "Thevaa", family = "Chandereng", role = c("aut", "cre", "cph"),
email = "chandereng@wisc.edu", comment = c(ORCID = "0000-0003-4078-9176")),
person("Donald", "Musgrove", email = "donald.r.musgrove@medtronic.com", role = c("aut", "cph")),
person("Tarek", "Haddad", email = "tarek.d.haddad@medtronic.com", role = c("aut", "cph")),
person("Graeme", "Hickey", email = "graeme.l.hickey@medtronic.com", role = c("aut", "cph")),
person("Timothy", "Hanson", email = "tim.hanson2@medtronic.com", role = c("aut", "cph")),
person("Theodore", "Lystig", email = "theodore.lystig@medtronic.com", role = c("aut", "cph")))
Simulation and analysis of Bayesian adaptive clinical trial, incorporates historical
data and allows early stopping for futility or early success.
tidyr, dplyr, knitr, bayesDP, parallel, purrr, msm, magrittr,
devtools
Thevaa Chandereng [aut, cre, cph]
(<https://orcid.org/0000-0003-4078-9176>),
Donald Musgrove [aut, cph],
Tarek Haddad [aut, cph],
Graeme Hickey [aut, cph],
Timothy Hanson [aut, cph],
Theodore Lystig [aut, cph]

testthat, rmarkdown, pkgdown, devtools, knitr

bayesDP, dplyr, purrr, msm, survival, magrittr

Simulation and analysis of Bayesian adaptive clinical trials for binomial, Gaussian, and time-to-event data types,
incorporates historical data and allows early stopping for futility or early success. The package uses novel
and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up,
and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data
into the analysis via the power prior or non-informative priors.
bayesDP, dplyr, purrr, survival, magrittr (>= 1.5)