Package: OmicKriging 1.4.0

Hae Kyung Im

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.

Authors:Hae Kyung Im, Heather E. Wheeler, Keston Aquino Michaels, Vassily Trubetskoy

OmicKriging_1.4.0.tar.gz
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OmicKriging.pdf |OmicKriging.html
OmicKriging/json (API)

# Install 'OmicKriging' in R:
install.packages('OmicKriging', repos = c('https://hakyimlab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hakyimlab/omickriging/issues

On CRAN:

Conda:

4.38 score 2 stars 48 scripts 155 downloads 1 mentions 8 exports 13 dependencies

Last updated 4 years agofrom:48edf855f1. Checks:9 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesFAILMar 20 2025
R-4.5-winERRORMar 20 2025
R-4.5-macERRORMar 20 2025
R-4.5-linuxERRORMar 20 2025
R-4.4-winERRORMar 20 2025
R-4.4-macERRORMar 20 2025
R-4.4-linuxERRORMar 20 2025
R-4.3-winERRORMar 20 2025
R-4.3-macERRORMar 20 2025

Exports:krigr_cross_validationload_sample_datamake_GXMmake_PCs_irlbamake_PCs_svdokrigingread_GRMBinwrite_GRMBin

Dependencies:bitopscaToolscodetoolsdoParallelforeachgplotsgtoolsirlbaiteratorsKernSmoothlatticeMatrixROCR