Introduction

BayesianModeling is a java software development tool to generate syntax of software to generate syntax of several models of Binary Regression and Item Response Theory under a Bayesian approach using Markov chain Monte Carlo (MCMC) methods. Subsequently, the syntax can be executed in the programs OpenBUGS (Spiegelhalter, Thomas, Best, Lunn, 2007), WinBUGS (Spiegelhalter, Thomas, Best, Lunn, 2003) or in R program (R Development Core Team, 2004) through the libraries R2WinBUGS and R2OpenBUGS (Sturtz, Ligges and Gelman, 2005), BRugs (Thomas, et al, 2006) and rbugs.

This application is for your personal use and must not be used for any commercial purpose whatsoever without my explicit written permission. The application is provided “as is” without warranty of any kind. In order appropriately to implement the different models mentioned in the application, you it must read in detail the literature suggested in the references and must be familiarized with the Bayesian Inference using MCMC.

BayesianModeling is thought for practitioners that given a data set they wish to know the syntax of diverse Binary Regression or Item Response models in bugs code, Theory usually non available in diverse statistical programs including the program R. This program write two files: a bugs model file for each one of this models considering adequate priors, lists with sensible starting values and size of the data set and a data set file in rectangular format, both readable in WinBUGS or OpenBUGS.

This basic application can be considered a different version of BRMUW (Bazán, 2010) which was developed as part of the projects DAI 3412, 4031 and 2009-0033 of Pontifical Catholic University of Peru with the purpose to disseminate models developed by the author.

This application together with some models has been developed for three late years and throughout that time various people have collaborated in this project reason why I desire to express my gratefulness. Among them to the colleagues Oscar Millones, Christian Bayes and Miluska Osorio for theirs aid during the revision of the present version of the application and the user guide. I also am thankful to Martin Iberico, Margareth Sequeiros and Pedro Curich for the computational support in some of the stages of the project. Thank you very much to my family for its patience and support.

Installation instructions can be found in the included README file. BayesianModeling can run smoothly in any operating systems, such as Windows, Mac OS, Linux, in which the Java Virtual Machine and Perl are supported.

Java: Java SE Runtime Environment 1.6 or later. Perl: Perl v5.10 or later. RAM: ≥ 512M

The BayesianModeling package was introduced at the II Conbratri-2011 (http://187.45.202.74/conbratri/).