Imports: ggplot2 (>= 3.3.2), methods, stats

Title: Efficient Sampling for Gaussian Linear Regression with Arbitrary
Author: P. Richard Hahn, Jingyu He and Hedibert Lopes
Description: Efficient sampling for Gaussian linear regression with arbitrary priors.

Imports: Rcpp (>= 0.12.7), stats, graphics, grDevices, coda

Author: P. Richard Hahn, Jingyu He, Hedibert Lopes
Imports: Rcpp (>= 0.12.7), stats, graphics, grDevices, coda,

Description: Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Hedibert (2018) <arXiv:1806.05738>.

person("Jingyu", "He", , "jingyuhe@cityu.edu.hk", c("aut", "cre")),
person("P. Richard", "Hahn", role = "aut"),
person("Hedibert", "Lopes", role = "aut"),
person("Andrew", "Herren", role = "ctb"))
Description: Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) <arXiv:1806.05738>.
Imports: Rcpp (>= 0.12.7), stats, graphics, grDevices, coda, methods,

Title: Bayesian Analysis of Contingency Table Data
Description: The function MC3() searches for log-linear models with the highest posterior probability. The function gibbsSampler() is a blocked Gibbs sampler for sampling from the posterior distribution of the log-linear parameters. The functions findPostMean() and findPostCov() compute the posterior mean and covariance matrix for decomposable models which, for these models, is available in closed form.

Title: Adjust Longitudinal Regression Models Using Bayesian Methodology
Maintainer: Edwin Javier Castillo Carreño <edjcastilloca@unal.edu.co>
Description: Adjusts longitudinal regression models using Bayesian methodology
for covariance structures of composite symmetry (SC),
autoregressive ones of order 1 AR (1) and
autoregressive moving average of order (1,1) ARMA (1,1).
Depends: R(>= 3.1.0), LearnBayes, mvtnorm, MASS

Description: bayesm covers many important models used
in marketing and micro-econometrics applications.
Bayes Regression (univariate or multivariate dep var),
Multinomial Logit (MNL) and Multinomial Probit (MNP),
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Multinomial Logits with mixture of normals prior
Bayesian analysis of choice-based conjoint data,
Bayesian treatment of linear instrumental variables models,
Analyis of Multivariate Ordinal survey data with scale
usage heterogeneity (as in Rossi et al, JASA (01)).
For further reference, consult our book, Bayesian Statistics and
Marketing by Allenby, McCulloch and Rossi.
Packaged: Thu Apr 21 22:47:39 2005; hornik