Description: Best Orthogonalized Subset Selection (BOSS) is a least-squares (LS) based subset selection method, that performs best subset selection upon an orthogonalized basis of ordered predictors, with the computational effort of a single ordinary LS fit. This package provides a highly optimized implementation of BOSS and estimates a heuristic degrees of freedom for BOSS, which can be plugged into an information criterion (IC) such as AICc in order to select the subset from candidates. It provides various choices of IC, including AIC, BIC, AICc, Cp and GCV. It also implements the forward stepwise selection (FS) with no additional computational cost, where the subset of FS is selected via cross-validation (CV). CV is also an option for BOSS. For details see: Tian, Hurvich and Simonoff (2021), "On the Use of Information Criteria for Subset Selection in Least Squares Regression", <arXiv:1911.10191>.
Suggests: devtools, ISLR, kableExtra, knitr, MASS, rmarkdown, sparsenet

Title: Bayesian Phylogeographic and Ecological Clustering
Description: 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.
Packaged: 2016-04-06 07:05:23 UTC; ioannamanolopoulou

Packaged: 2018-04-03 20:10:56 UTC; ioannamanolopoulou

Packaged: 2018-03-22 17:16:44 UTC; ioannamanolopoulou

Packaged: 2018-08-29 14:31:56 UTC; ioannamanolopoulou

Description: 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.
Packaged: 2020-02-29 10:43:37 UTC; ioannamanolopoulou

Title: Bayesian Prediction with High-order Interactions
Description: 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.
Packaged: Wed Feb 20 23:53:11 2008; longhai