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
DOI:	10.32614/CRAN.package.OmicKriging
Author:	Hae Kyung Im, Heather E. Wheeler, Keston Aquino Michaels, Vassily Trubetskoy
Maintainer:	Hae Kyung Im <haky at uchicago.edu>
License:	GPL (≥ 3)
NeedsCompilation:	no
Materials:	README
CRAN checks:	OmicKriging results

Documentation:

Reference manual:	OmicKriging.pdf
Vignettes:	Application Tutorial: OmicKriging

Downloads:

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
macOS binaries:	r-release (arm64): OmicKriging_1.4.0.tgz, r-oldrel (arm64): OmicKriging_1.4.0.tgz, r-release (x86_64): OmicKriging_1.4.0.tgz, r-oldrel (x86_64): OmicKriging_1.4.0.tgz
Old sources:	OmicKriging archive

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