BAMBI: An R package for Fitting Bivariate Angular Mixture Models
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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 using Bayesian methods. In this R package, we implement 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. BAMBI provides 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.
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