OmicKriging: Poly-Omic Prediction of Complex TRaits

It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y.

Version: 1.4.0
Depends: R (≥ 2.15.1), doParallel
Imports: ROCR, irlba, parallel, foreach
Published: 2016-03-08
Author: Hae Kyung Im, Heather E. Wheeler, Keston Aquino Michaels, Vassily Trubetskoy
Maintainer: Hae Kyung Im <haky at uchicago.edu>
BugReports: NA
License: GPL (≥ 3)
URL: NA
NeedsCompilation: no
Materials: README
CRAN checks: OmicKriging results

Downloads:

Reference manual: OmicKriging.pdf
Vignettes: Application Tutorial: OmicKriging
Package source: OmicKriging_1.4.0.tar.gz
Windows binaries: r-devel: OmicKriging_1.4.0.zip, r-release: OmicKriging_1.4.0.zip, r-oldrel: OmicKriging_1.4.0.zip
OS X Mavericks binaries: r-release: OmicKriging_1.4.0.tgz, r-oldrel: OmicKriging_1.4.0.tgz
Old sources: OmicKriging archive