COUSCOus: A Residue-Residue Contact Detecting Method

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) <doi:10.1214/aos/1176345010> 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.

Version: 1.0.0
Depends: R (≥ 3.2.2)
Imports: bio3d (≥ 2.2-2), matrixcalc (≥ 1.0-3), utils (≥ 3.2.2)
Published: 2016-02-28
Author: Reda Rawi [aut,cre], Matyas A. Sustik [aut], Ben Calderhead [aut]
Maintainer: Reda Rawi <rrawi at qf.org.qa>
License: GPL (≥ 3)
NeedsCompilation: yes
CRAN checks: COUSCOus results

Downloads:

Reference manual: COUSCOus.pdf
Package source: COUSCOus_1.0.0.tar.gz
Windows binaries: r-devel: COUSCOus_1.0.0.zip, r-release: COUSCOus_1.0.0.zip, r-oldrel: COUSCOus_1.0.0.zip
OS X El Capitan binaries: r-release: COUSCOus_1.0.0.tgz
OS X Mavericks binaries: r-oldrel: COUSCOus_1.0.0.tgz

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