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 |
| 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 |
| macOS binaries: | r-release: COUSCOus_1.0.0.tgz, r-oldrel: COUSCOus_1.0.0.tgz |
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