Baysian Modeling and Data Analysis in S-PLUS

RES900 – NIH “S-Plus Bayes II”

The ultimate goal of this project is to provide an extensive suite of Bayesian statistical software tools, utilizing a fast and effective Markov chain Monte Carlo (MCMC) computation engine. The overall implementation will be in the S-PLUS object-oriented language and system for statistical modeling and data analysis, and the MCMC engine will be implemented in C or C++, with an efficient interface to S-PLUS. The implementation will emphasize an ease-of-use paradigm that strongly encourages routine use of Bayesian methods as well as research-oriented exploration for new Statistical techniques by S-PLUS. We will develop Bayesian methods for the most widely used statistical models such as a hierarchical linear regression models, generalized linear mixed models, missing data models and models for robust inference. A large percentage of statisticians in the United States are employed in biostatistics and allied “bio” industries, and a considerable amount of statistical education and research occurs in medical and health related fields. The availability of a broad range of Bayesian statistical methods in a commercially viable data analysis product such as S-PLUS, will provide an important service to these industries, and to the research and educational needs by supporting and advancing the emerging paradigm of Bayesian modeling and data analysis.