Provide an easy and automatic way to construct Markov State Models of biological datasets.
Constructing human comprehensible Markov state models(MSMs) to understand the dynamics of biomolecules can be a difficult task, mainly due to the rouged nature of its inherent energy landscape. This project provides a python implementation of the Super-level-set Hierarchical Clustering algorithm, originally described by Huang et. al. This program automates the discovery of metastable states and is intended to be an extension to the SimTK MSMbuilder.