Hierarchical Bayesian Model for a Hazard Function
person("Andrew", "Chapple", email = "Andrew.G.Chapple@rice.edu", role = c("aut", "cre"))
Fits a piecewise exponential hazard to survival data using a
Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
formulation for the spatial dependency in the hazard rates for each piece.
This function uses Metropolis- Hastings-Green MCMC to allow the number of split
points to vary. This function outputs graphics that display the histogram of
the number of split points and the trace plots of the hierarchical parameters.
The function outputs a list that contains the posterior samples for the number
of split points, the location of the split points, and the log hazard rates
corresponding to these splits. Additionally, this outputs the posterior samples
of the two hierarchical parameters, Mu and Sigma^2.

Generate Postestimation Quantities for Bayesian MCMC Estimation
c(person("Johannes", "Karreth", email = "jkarreth@ursinus.edu", role = c("aut")),
person("Shana", "Scogin", email = "shanarscogin@gmail.com", role = c("aut", "cre")),
person("Andreas", "Beger", email = "adbeger@gmail.com", role = c("aut")),
person("Myunghee", "Lee", email = "mlq38@mail.missouri.edu", role = c("ctb")),
person("Neil", "Williams", email = "snpwill@uga.edu", role = c("ctb")))
An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'.
carData, caTools, coda (>= 0.13), dplyr (>= 0.5.0), ggmcmc,
ggplot2, ggridges, R2jags, reshape2, rlang, ROCR, stats, tidyr
(>= 0.5.1)
knitr, MCMCpack, rmarkdown, rstan (>= 2.10.1), rstanarm,
testthat
Johannes Karreth [aut],
Shana Scogin [aut, cre],
Andreas Beger [aut],
Myunghee Lee [ctb],
Neil Williams [ctb]