De Waal, DaanHarris, TristanDe Waal, AltaMazarura, Jocelyn Rangarirai2023-09-072023-09-072022-07-06De Waal, D.; Harris, T.; De Waal, A.; Mazarura, J. Modelling Bimodal Data Using a Multivariate Triangular-Linked Distribution. Mathematics 2022, 10, 2370. https://DOI.org/10.3390/math10142370.2227-739010.3390/math10142370http://hdl.handle.net/2263/92243Bimodal distributions have rarely been studied although they appear frequently in datasets. We develop a novel bimodal distribution based on the triangular distribution and then expand it to the multivariate case using a Gaussian copula. To determine the goodness of fit of the univariate model, we use the Kolmogorov–Smirnov (KS) and Cramér–von Mises (CVM) tests. The contributions of this work are that a simplistic yet robust distribution was developed to deal with bimodality in data, a multivariate distribution was developed as a generalisation of this univariate distribution using a Gaussian copula, a comparison between parametric and semi-parametric approaches to modelling bimodality is given, and an R package called btld is developed from the workings of this paper.en© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.BimodalityTriangular distributionsRandom generationCopulasMixture modelsArticle