Packaged: 2021-12-19 07:51:36 UTC; kuo-junglee

Packaged: 2022-05-09 15:03:10 UTC; kuojunglee

Title: Bayes Factors for Hierarchical Linear Models with Continuous
Authors@R: c(person(given="Mirko", family="Thalmann", role=c("aut", "cre"),
Imports: rjags, ggplot2, metRology, grid, reshape, methods, coda
Description: Runs hierarchical linear Bayesian models. Samples from the posterior
distributions of model parameters in JAGS (Just Another Gibbs Sampler;
Plummer, 2003, <http://mcmc-jags.sourceforge.net/>). Computes Bayes factors for
group parameters of interest with the Savage-Dickey density ratio (Wetzels,
Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).

Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group
parameters of interest with the Savage-Dickey density ratio (Wetzels,

Title: Fits the Bayesian Piecewise Linear Log-Hazard Model
Maintainer: Andrew G Chapple <achapp@lsuhsc.edu>
Description: Contains posterior samplers for the Bayesian piecewise linear log-hazard and piecewise exponential hazard models, including Cox models. Posterior mean restricted survival times are also computed for non-Cox an Cox models with only treatment indicators. The ApproxMean() function can be used to estimate restricted posterior mean survival times given a vector of patient covariates in the Cox model. Functions included to return the posterior mean hazard and survival functions for the piecewise exponential and piecewise linear log-hazard models. Chapple, AG, Peak, T, Hemal, A (2020). Under Revision.

Title: Bayesian Variable Selection Using Simplified Shotgun Stochastic
Description: In p >> n settings, full posterior sampling using existing Markov chain Monte
Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical
perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities).