Package: RLMM 1.75.0

Nusrat Rabbee

RLMM: A Genotype Calling Algorithm for Affymetrix SNP Arrays

A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now.

Authors:Nusrat Rabbee <[email protected]>, Gary Wong <[email protected]>

RLMM_1.75.0.tar.gz
RLMM_1.75.0.zip(r-4.7)RLMM_1.75.0.zip(r-4.6)RLMM_1.75.0.zip(r-4.5)
RLMM_1.75.0.tgz(r-4.6-any)RLMM_1.75.0.tgz(r-4.5-any)
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RLMM_1.75.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RLMM/json (API)

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

On BioConductor:RLMM-1.75.0(bioc 3.24)RLMM-1.74.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

microarrayonechannelsnpgeneticvariability

3.90 score 1 scripts 379 downloads 4 mentions 4 exports 1 dependencies

Last updated from:658fea24ad. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksERROR129
linux-devel-x86_64NOTE118
source / vignettesOK134
linux-release-x86_64NOTE122
macos-release-arm64NOTE94
macos-oldrel-arm64NOTE102
windows-develNOTE83
windows-releaseNOTE65
windows-oldrelNOTE58
wasm-releaseOK92

Exports:Classifycreate_Thetafilenormalize_Rawfilesplot_theta

Dependencies:MASS

RLMM Doc

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Last update: 2013-11-01
Started: 2013-11-01