Bayesian Distribution Regression

Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.

An R package for Bayesian Distribution Regression. This package was first used for analyses in the paper "Bayesian Distribution Regression" by Weige Huang and Emmanuel Selorm Tsyawo. The paper is the recommended citation and reference for the package bayesdistreg. The package implements routines in the paper including the three Bayesian Distribution Regression estimators namely, the Non-asymptotic, Semi-Asymptotic, and Asymptotic BDR. Link to the package webpage


bayesdistreg 0.1.0

  • Initial code for Bayesian Distribution Regression

Reference manual

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0.1.0 by Emmanuel Tsyawo, 2 months ago

Browse source code at

Authors: Emmanuel Tsyawo [aut, cre] , Weige Huang [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports MASS, sandwich, stats

See at CRAN