PyEMMA is a Python library for the estimation, validation, and analysis of kinetic models from MD data. Functionalities include dimension reduction techniques such as the time-lagged independent component analysis, clustering, maximum-likelihood and Bayesian estimation of Markov State Models and Hidden Markov Models, coarse-graining and analysis of kinetic models, computation of transition pathways, and visualization tools.

Code: github.com/markovmodel/pyemma
Languages: Python (90%), C (10%)
Usage: > 50,000 downloads
License: Lesser GPL (open-source)
Release: 2.4, 34 releases
Platforms: Linux, Windows, Mac
DOI: 10.1021/acs.jctc.5b00743
DOI: 10.1021/ct300274u

The one-week PyEMMA winter school in February is open to students and researchers. 

For more detail see past winter school programs and our youtube channel.