Bayesian Phylogeographic and Ecological Clustering
Model-based clustering for phylogeographic data comprising mtDNA sequences and geographical locations along with optional environmental characteristics, aiming to identify migration events that led to homogeneous population clusters.

Model-based clustering for phylogeographic data comprising mtDNA sequences and geographical locations along with optional environmental characteristics, aiming to identify migration events that led to homogeneous population clusters. The package vignette, I. Manolopoulou, A. Hille, B. C. Emerson (2020) <doi:10.18637/jss.v092.i03>, provides detailed descriptions of the package.

Bayesian Prediction with High-order Interactions
This software is 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 discreate response  based on a number of discreate covariates. In both situations, we use Bayesian logistic regression models and consider the high-order interactions in making prediction. 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. In this version, we use log-normal prior for the hyperparameters. When it is used for the second situation --- classification, in this version we consider the full set of interaction patterns up to a prespecified order.

This software is 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 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. In this version, we use log-normal prior for the hyperparameters. When it is used for the second situation --- classification, we consider the full set of interaction patterns up to a specified order.