Learn R Programming

COUSCOus (version 1.0.0)

A Residue-Residue Contact Detecting Method

Description

Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.

Copy Link

Version

Install

install.packages('COUSCOus')

Monthly Downloads

16

Version

1.0.0

License

GPL (>= 3)

Maintainer

Last Published

February 28th, 2016

Functions in COUSCOus (1.0.0)

COUSCOus

Contact prediction using shrinked covariance.
COUSCOus-internal

Internal COUSCOus functions