  We present $\textbf{PyRMLE}$, a Python module that implements Regularized
Maximum Likelihood Estimation for the analysis of Random Coefficient models.
$\textbf{PyRMLE}$ is simple to use and readily works with data formats that are
typical to Random Coefficient problems. The module makes use of Python's
scientific libraries $\textbf{NumPy}$ and $\textbf{SciPy}$ for computational
efficiency. The main implementation of the algorithm is executed purely in
Python code which takes advantage of Python's high-level features.
