BAS (version 0.85)

BAS-package: Bayesian Model Averaging using Bayesian Adaptive Sampling

Description

Package for Bayesian Model Averaging in linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are of the form of Zellner's g-prior or mixtures of g-priors. Options include the Zellner-Siow Cauchy Priors, the Liang et al hyper-g priors, Local and Global Empirical Bayes estimates of g, and other default model selection criteria such as AIC and BIC. Sampling probabilities may be updated based on the sampled models.

Details

ll{ Package: BAS Version: 0.4 Date: 2009-12-23 Depends: R (>= 2.6) License: GPL-2 URL: http://www.stat.duke.edu/~clyde }

Index:

References

Clyde, M. Ghosh, J. and Littman, M. (2009) Bayesian Adaptive Sampling for Variable Selection and Model Averaging. Department of Statistical Science Discussion Paper 2009-16. Duke University. Clyde, M. and George, E. I. (2004) Model uncertainty. Statist. Sci., 19, 81-94. http://www.isds.duke.edu/~clyde/papers/statsci.pdf

Clyde, M. (1999) Bayesian Model Averaging and Model Search Strategies (with discussion). In Bayesian Statistics 6. J.M. Bernardo, A.P. Dawid, J.O. Berger, and A.F.M. Smith eds. Oxford University Press, pages 157-185.

Liang, F., Paulo, R., Molina, G., Clyde, M. and Berger, J.O. (2005) Mixtures of g-priors for Bayesian Variable Selection. http://www.stat.duke.edu/05-12.pdf

See Also

bas

Examples

demo(BAS.USCrime)
demo(BAS.hald)