Skip to main content

Python package for implementing Regularized Maximum Likelihood for Random Coefficient Models

Project description


Authors: Mendoza, E., Dunker, F., Reale, M.

PyRMLE is a Python Module that implements Regularized Maximum Likelihood Estimation for the Random Coefficients Model.

The package’s implementation of regularized maximum likelihood is limited to applications with up to two regressors for the random coefficients model with intercept, and up to three regressors for a model without intercept.

There are two main functions used to implement regularized maximum likelihood estimation using PyRMLE, namely: (1) transmatrix() which executes a finite-volume algorithm that is akin to an algebraic reconstruction of the Radon Transform, and (2) rmle() which is a wrapper function of the scipy.optimize.minimize() function that solves the constrained regularized maximum likelihood problem.

For more details check the github repository here:

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyrmle-0.0.3.post9.tar.gz (20.3 kB view hashes)

Uploaded Source

Built Distribution

pyrmle-0.0.3.post9-py3-none-any.whl (20.4 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page