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.

翻译：我们展示了$\textbf{PyRMLE}$\ textbf{PyRMLE}, 这是用于分析随机节能模型的固定最大可能性估计的 Python 模块。 $\ textbf{PyRMLE}$\ textbf{PyRMLE} 简单易用, 并使用随机节能问题所特有的数据格式。 该模块利用 Python 的科学图书馆$\ textbf{NumPy} $ 和$\ textbf{SciPy} 来计算效率。 算法的主要实施纯粹在利用 Python 高级功能的 Python 代码中进行 。