BAMBI: An R Package for Fitting Bivariate Angular Mixture Models
Dublin Core
Title
BAMBI: An R Package for Fitting Bivariate Angular Mixture Models
Subject
angular data, mixture models, bivariate data, von Mises distribution, wrapped
normal distribution, R, Hamiltonian Monte Carlo, MCMC, Gibbs sampler
normal distribution, R, Hamiltonian Monte Carlo, MCMC, Gibbs sampler
Description
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. More specifically, the bivariate wrapped normal, von Mises sine and von Mises cosine distributions,
and mixtures thereof, have been proposed for practical use. However, there is a lack of
software implementing these distributions and the associated inferential techniques. In
this article, we introduce BAMBI, an R package for analyzing bivariate (and univariate)
angular data. We implement random data generation, density evaluation, and computation of theoretical summary measures (variances and correlation coefficients) for the three
aforementioned bivariate angular distributions, as well as two univariate angular distributions: the univariate wrapped normal and the univariate von Mises distribution. 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 data sets included
in the package, one univariate and one bivariate
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. More specifically, the bivariate wrapped normal, von Mises sine and von Mises cosine distributions,
and mixtures thereof, have been proposed for practical use. However, there is a lack of
software implementing these distributions and the associated inferential techniques. In
this article, we introduce BAMBI, an R package for analyzing bivariate (and univariate)
angular data. We implement random data generation, density evaluation, and computation of theoretical summary measures (variances and correlation coefficients) for the three
aforementioned bivariate angular distributions, as well as two univariate angular distributions: the univariate wrapped normal and the univariate von Mises distribution. 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 data sets included
in the package, one univariate and one bivariate
Creator
Saptarshi Chakraborty
Source
https://www.jstatsoft.org/article/view/v099i11
Publisher
State University of New York at Buffalo
Date
August 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Saptarshi Chakraborty, “BAMBI: An R Package for Fitting Bivariate Angular Mixture Models,” Repository Horizon University Indonesia, accessed March 14, 2025, https://repository.horizon.ac.id/items/show/8212.