doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.">

GGoutlieR: Identify Individuals with Unusual Geo-Genetic Patterns (original) (raw)

Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.

Version: 1.0.2
Depends: R (≥ 3.5.0)
Imports: stats4, FastKNN, foreach, doParallel, parallel, scales, RColorBrewer, ggforce, rlang, stats, tidyr, utils, rnaturalearth, sf, ggplot2, cowplot
Suggests: rnaturalearthdata
Published: 2023-10-15
DOI: 10.32614/CRAN.package.GGoutlieR
Author: Che-Wei Chang ORCID iD [aut, cre], Karl Schmid ORCID iD [ths]
Maintainer: Che-Wei Chang
License: MIT + file
NeedsCompilation: no
In views: AnomalyDetection
CRAN checks: GGoutlieR results

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