Title: Computations Around Bayesian Predictive Power
Author: Kaspar Rufibach, Paul Jordan, Markus Abt
Description: Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.

Title: Fitting Bayesian Poisson Regression
Description: Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2021) <arXiv:2109.09520>.
Imports: Rcpp (>= 1.0.7), RcppArmadillo, coda, MASS

Description: Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2022) <doi:10.1080/10618600.2022.2123337>.

Title: Bayesian Quantile Regression for Ordinal Models
Imports: MASS, pracma, tcltk, GIGrvg, truncnorm, NPflow, invgamma
Authors@R: c(person("Dr. Mohammad Arshad Rahman", "Developer", role = "aut",
person("Prajual Maheshwari", role = "cre", email = "prajual1391@gmail.com"))
Description: Provides an estimation technique for Bayesian quantile
regression in ordinal models. Two algorithms are considered - one
for an ordinal model with three outcomes and the other for an ordinal
model with more than 3 outcomes. It further provides model performance
criteria and trace plots for Markov chain Monte Carlo (MCMC) draws.
Author: Dr. Mohammad Arshad Rahman Developer [aut],

Imports: MASS, pracma, tcltk, GIGrvg, truncnorm, NPflow, invgamma,
Authors@R: c(person("Prof. Mohammad Arshad Rahman", "Developer", role = "aut",
Author: Prof. Mohammad Arshad Rahman Developer [aut],

Imports: MASS, pracma, GIGrvg, truncnorm, NPflow, invgamma, graphics,
Description: Package provides functions for estimating Bayesian quantile regression with ordinal outcomes,
computing the covariate effects, model comparison measures, and inefficiency factor. The generic
ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs
sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes
(labeled OR2 model) is estimated using Gibbs sampling only. For each model framework, there is a
specific function for estimation. The summary output produces estimates for regression quantiles
and two measures of model comparison — log of marginal likelihood and Deviance Information Criterion
(DIC). The package also has specific functions for computing the covariate effects and other functions
that aids either the estimation or inference in quantile ordinal models.
Packaged: 2022-07-06 12:40:30 UTC; prajualmaheshwari