Description: Bayesian model averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison.

Author: Martin Feldkircher and Stefan Zeugner and Paul Hofmarcher
Depends: methods, stats, graphics, R (>= 2.10)
Description: Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.

Title: Bayesian Model Selection under Constraints
person("Marcus", "Groß", email = "marcus.gross@inwt-statistics.de", role = c("aut", "cre")),
person("Ricardo", "Fernandes", email = "ldv1452@gmail.com", role = c("aut")),
person("Mira Celine", "Klein", email = "mira.klein@inwt-statistics.de", role = c("ctb"))
Description: A Bayesian regression package supporting constrained coefficient estimation and
variable selection using Stan. This includes a robust variable selection
algorithm by a horseshoe prior  (<doi:10.1093/biomet/asq017>) that finds the optimal model considering main effects,
interactions as well as powers of given variables under potential parameter constraints.