Bayesian Prediction with High-order Interactions CRAN Task View: Bayesian (http://CRAN.R-project.org/view=Bayesian) CRAN Task View: MachineLearning (http://CRAN.R-project.org/view=MachineLearning) This software can be used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate covariates. In both situations, we use Bayesian logistic regression models that consider the high-order interactions. The models are trained with slice sampling method, a variant of Markov chain Monte Carlo. The time arising from using high-order interactions is reduced greatly by our compression technique that represents a group of original parameters as a single one in MCMC step. Software Depends: R (>= 2.5.1) Longhai Li <longhai@math.usask.ca> Longhai Li <longhai@math.usask.ca> GPL (>= 2) 2009-08-01 application/tgz http://CRAN.R-project.org/package=BPHO