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.

Statistical analysis software