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Lang, S. and Brezger, Andreas (2000): BayesX - Software for Bayesian Inference based on Markov Chain Monte Carlo simulation techniques. Collaborative Research Center 386, Discussion Paper 187
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Abstract

BayesX is a Software tool for Bayesian inference based on Markov Chain Monte Carlo (MCMC) inference techniques. The main feature of BayesX so far, is a very powerful regression tool for Bayesian semiparametric regression within the Generalized linear models framework. BayesX is able to estimate nonlinear effects of metrical covariates, trends and flexible seasonal patterns of time scales, structured and/or unstructured random effects of spatial covariates (geographical data) and unstructured random effects of unordered group indicators. Moreover, BayesX is able to estimate varying coefficients models with metrical and even spatial covariates as effectmodifiers. The distribution of the response can be either Gaussian, binomial or Poisson. In addition, BayesX has some useful functions for handling and manipulating datasets and geographical maps.