Skip to main content


Project description

BICePs - Bayesian Inference of Conformational Populations

The BICePs algorithm (Bayesian Inference of Conformational Populations) is a statistically rigorous Bayesian inference method to reconcile theoretical predictions of conformational state populations with sparse and/or noisy experimental measurements and objectively compare different models. Supported experimental observables include:

Citation DOI for Citing BICePs

Check our BICePs website for more details!

Please check out the theory of BICePs to learn more.

Installation (in progress)

BICePs supports Python 2.7 or Python 3.4+ (coming soon) on Mac, Linux, and Windows.

Dependencies of BICePs

  • Pymbar --> 3.0 or later
  • MDTraj --> 1.9 or later
  • Matplotlib --> 1.5.1 or later
  • Numpy --> 1.14.0 or later

View the workflow of BICePs.

BICePs is research software. If you make use of BICePs in scientific publications, please cite it.

To get started, see BICePs_2.0 for the latest version of BICePs.

Project details

Download files

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

Files for biceps, version 2.0b4
Filename, size File type Python version Upload date Hashes
Filename, size biceps-2.0b4-py2-none-any.whl (56.8 kB) File type Wheel Python version py2 Upload date Hashes View hashes
Filename, size biceps-2.0b4.tar.gz (44.6 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page