Metadata-Version: 2.1
Name: bhmm
Version: 0.6.3
Summary: BHMM: A toolkit for Bayesian hidden Markov model analysis of single-molecule trajectories.
Home-page: https://github.com/bhmm/bhmm
Author: John Chodera and Frank Noe
Author-email: john.chodera@choderalab.org
License: LGPL
Platform: Linux
Platform: Mac OS-X
Platform: Unix
Platform: Windows
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: msmtools
Requires-Dist: six

This project provides tools for estimating the number of metastable states, rate
constants between the states, equilibrium populations, distributions
characterizing the states, and distributions of these quantities from
single-molecule data. This data could be FRET data, single-molecule pulling
data, or any data where one or more observables are recorded as a function of
time. A Hidden Markov Model (HMM) is used to interpret the observed dynamics,
and a distribution of models that fit the data is sampled using Bayesian
inference techniques and Markov chain Monte Carlo (MCMC), allowing for both the
characterization of uncertainties in the model and modeling of the expected
information gain by new experiments.


