Title: BAMBI: An R package for Fitting Bivariate Angular Mixture Models,
Abstract: Statistical analyses of directional or angular data have applications in a
variety of fields, such as geology, meteorology and bioinformatics. There is
substantial literature on descriptive and inferential techniques for univariate
angular data, with the bivariate (or more generally, multivariate) cases
receiving more attention in recent years. However, there is a lack of software
implementing inferential techniques in practice, especially in the bivariate
situation. In this article, we introduce *BAMBI*, an R package for analyzing
bivariate (and univariate) angular data. We implement random generation,
density evaluation, and computation of summary measures for three bivariate
(viz., bivariate wrapped normal, von Mises sine and von Mises cosine) and two
univariate (viz., univariate wrapped normal and von Mises) angular
distributions. The major contribution of BAMBI to statistical computing is in
providing Bayesian methods for modeling angular data using finite mixtures of
these distributions. We also provide functions for visual and numerical
diagnostics and Bayesian inference for the fitted models. In this article, we
first provide a brief review of the distributions and techniques used in BAMBI,
then describe the capabilities of the package, and finally conclude with
demonstrations of mixture model fitting using BAMBI on the two real datasets
included in the package, one univariate and one bivariate.