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Most of applied statistics involves regression analysis of data. This paperpresents a stand-alone and menu-driven software package, Bayesian Regression:Nonparametric and Parametric Models. Currently, this package gives the user achoice from 83 Bayesian models for data analysis. They include 47 Bayesiannonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixturemodels for density estimation; and 31 normal random effects models (HLMs),including normal linear models. Each of the 78 regression models handles eithera continuous, binary, or ordinal dependent variable, and can handle multi-level(grouped) data. All 83 Bayesian models can handle the analysis of weightedobservations (e.g., for meta-analysis), and the analysis of left-censored,right-censored, and/or interval-censored data. Each BNP infinite-mixture modelhas a mixture distribution assigned one of various BNP prior distributions,including priors defined by either the Dirichlet process, Pitman-Yor process(including the normalized stable process), beta (two-parameter) process,normalized inverse-Gaussian process, geometric weights prior, dependentDirichlet process, or the dependent infinite-probits prior. The software usercan mouse-click to select a Bayesian model and perform data analysis via Markovchain Monte Carlo (MCMC) sampling. After the sampling completes, the softwareautomatically opens text output that reports MCMC-based estimates of themodel's posterior distribution and model predictive fit to the data. Additionaltext and/or graphical output can be generated by mouse-clicking other menuoptions. This includes output of MCMC convergence analyses, and estimates ofthe model's posterior predictive distribution, for selected functionals andvalues of covariates. The software, constructed from MATLAB Compiler, isillustrated through the BNP regression analysis of real data.

A Menu-Driven Software Package of Bayesian Nonparametric (and Parametric) Mixed Models for Regression Analysis and Density Estimation