BayesX: Analysing Bayesian structured additive regression models

There has been much recent interest in Bayesian inference for
generalized additive and related models. The increasing popularity
of Bayesian methods for these and other model classes is mainly
caused by the introduction of Markov chain Monte Carlo (MCMC)
simulation techniques which allow the estimation of very complex
and realistic models. This paper describes the capabilities of the
public domain software BayesX for estimating complex regression
models with structured additive predictor. The program extends the
capabilities of existing software for semiparametric regression.
Many model classes well known from the literature are special cases
of the models supported by BayesX. Examples are Generalized
Additive (Mixed) Models, Dynamic Models, Varying Coefficient
Models, Geoadditive Models, Geographically Weighted Regression and
models for space-time regression. BayesX supports the most common
distributions for the response variable. For univariate responses
these are Gaussian, Binomial, Poisson, Gamma and negative Binomial.
For multicategorical responses, both multinomial logit and probit
models for unordered categories of the response as well as
cumulative threshold models for ordered categories may be
estimated. Moreover, BayesX allows the estimation of complex
continuous time survival and hazardrate models.