  \texttt{Mixture-Models} is an open-source Python library for fitting Gaussian
Mixture Models (GMM) and their variants, such as Parsimonious GMMs, Mixture of
Factor Analyzers, MClust models, Mixture of Student's t distributions, etc. It
streamlines the implementation and analysis of these models using various
first/second order optimization routines such as Gradient Descent and Newton-CG
through automatic differentiation (AD) tools. This helps in extending these
models to high-dimensional data, which is first of its kind among Python
libraries. The library provides user-friendly model evaluation tools, such as
BIC, AIC, and log-likelihood estimation. The source-code is licensed under MIT
license and can be accessed at \url{https://github.com/kasakh/Mixture-Models}.
The package is highly extensible, allowing users to incorporate new
distributions and optimization techniques with ease. We conduct a large scale
simulation to compare the performance of various gradient based approaches
against Expectation Maximization on a wide range of settings and identify the
corresponding best suited approach.
