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Statistical analyses of directional or angular data have applications in avariety of fields, such as geology, meteorology and bioinformatics. There issubstantial literature on descriptive and inferential techniques for univariateangular data, with the bivariate (or more generally, multivariate) casesreceiving more attention in recent years. More specifically, the bivariatewrapped normal, von Mises sine and von Mises cosine distributions, and mixturesthereof, have been proposed for practical use. However, there is a lack ofsoftware implementing these distributions and the associated inferentialtechniques. In this article, we introduce BAMBI, an R package for analyzingbivariate (and univariate) angular data. We implement random data generation,density evaluation, and computation of theoretical summary measures (variancesand correlation coefficients) for the three aforementioned bivariate angulardistributions, as well as two univariate angular distributions: the univariatewrapped normal and the univariate von Mises distribution. The majorcontribution of BAMBI to statistical computing is in providing Bayesian methodsfor modeling angular data using finite mixtures of these distributions. We alsoprovide functions for visual and numerical diagnostics and Bayesian inferencefor the fitted models. In this article, we first provide a brief review of thedistributions and techniques used in BAMBI, then describe the capabilities ofthe package, and finally conclude with demonstrations of mixture model fittingusing BAMBI on the two real datasets included in the package, one univariateand one bivariate.

BAMBI: An R package for Fitting Bivariate Angular Mixture Models