dpGMM: A new R package for efficient and robust Gaussian mixture modeling of 1D and 2D data

Gaussian Mixture Modeling (GMM) is a powerful clustering and density estimation method with various applications in data analysis. We introduce an R package, dpGMM, a complete set of tools/procedures to analyze 1D or 2D data (binned or continuous), including the most efficient existing solutions to problems of fitting GMM to data by the recursive expectation-maximization (EM) algorithm. The effectiveness of the dpGMM package comes from leveraging the power of EM recursions by: (i) precise choice of the initial mixture parameters obtained with the use of the dynamic programming-based partition of data, and (ii) augmenting each M step with additional conditions aimed at preventing instability/divergence and accelerating the rate of convergence of iterations. dpGMM is implemented as a wrapper that allows for searching the best decomposition in the scenario with an unknown number of Gaussian mixture components, by using various information criteria, as well as with a fixed number of components. We compared dpGMM with three other R packages using synthetic and real biological datasets, performing large-scale computations to assess the performance of these GMM implementations across various scenarios.