Packaged: 2020-06-23 15:03:30 UTC; Matt Carli

Description: Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) <doi:10.1016/j.sste.2019.100286>.
Imports: coda, stats, rjags, stringr, plyr
Packaged: 2022-01-20 22:32:42 UTC; Matt Carli

Title: Bayesian Regression Model with Mixture of Two Scaled Inverse Chi
Maintainer: Renato Rodrigues Silva <renato.rrsilva@ufg.br>
Description: Functions to performs Bayesian regression model with mixture of two scaled inverse
chi square as hyperprior distribution for variance of each regression coefficient.

Description: Bayesian Latent Class Analysis using several different

Old Maintainer(s): Arthur White <arthur.white@ucdconnect.ie>

Authors@R: c( person( "Arthur", "White", email = "arwhite@tcd.ie", role = c( "aut", "cre" ), comment = "Previous email address: arthur.white@ucdconnect.ie" ), person( "Thomas Brendan", "Murphy", email = "brendan.murphy@ucd.ie", role = c( "aut", "ths" ) ) )
Author: Arthur White [aut, cre] (Previous email address:

Title: Bayesian Inference for Log-Normal Data
Authors@R: c(person("Aldo", "Gardini", email = "aldo.gardini2@unibo.it", role = c("aut", "cre")),
person("Enrico", "Fabrizi", role = c("aut")),
person("Carlo", "Trivisano", role = c("aut")))
Description: Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.
Imports: optimx, ghyp, fAsianOptions, coda, Rcpp (>= 0.12.17), MASS,