Package: assignPOP 1.3.0

assignPOP: Population Assignment using Genetic, Non-Genetic or Integrated Data in a Machine Learning Framework

Use Monte-Carlo and K-fold cross-validation coupled with machine- learning classification algorithms to perform population assignment, with functionalities of evaluating discriminatory power of independent training samples, identifying informative loci, reducing data dimensionality for genomic data, integrating genetic and non-genetic data, and visualizing results.

Authors:Kuan-Yu Chen [aut, cre], Elizabeth A. Marschall [aut], Michael G. Sovic [aut], Anthony C. Fries [aut], H. Lisle Gibbs [aut], Stuart A. Ludsin [aut]

assignPOP_1.3.0.tar.gz
assignPOP_1.3.0.zip(r-4.5)assignPOP_1.3.0.zip(r-4.4)assignPOP_1.3.0.zip(r-4.3)
assignPOP_1.3.0.tgz(r-4.5-any)assignPOP_1.3.0.tgz(r-4.4-any)assignPOP_1.3.0.tgz(r-4.3-any)
assignPOP_1.3.0.tar.gz(r-4.5-noble)assignPOP_1.3.0.tar.gz(r-4.4-noble)
assignPOP_1.3.0.tgz(r-4.4-emscripten)assignPOP_1.3.0.tgz(r-4.3-emscripten)
assignPOP.pdf |assignPOP.html✨
assignPOP/json (API)

# Install 'assignPOP' in R:

install.packages('assignPOP', repos = c('https://alexkychen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alexkychen/assignpop/issues

On CRAN:

cross-validation data-integration gbs machine-learning population-assignment population-genomics radseq

5.33 score 17 stars 25 scripts 610 downloads 11 mentions 13 exports 79 dependencies

Last updated 1 years agofrom:4f32e19db9. Checks:9 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 09 2025
R-4.5-win	OK	Mar 09 2025
R-4.5-mac	OK	Mar 09 2025
R-4.5-linux	OK	Mar 09 2025
R-4.4-win	OK	Mar 09 2025
R-4.4-mac	OK	Mar 09 2025
R-4.4-linux	OK	Mar 09 2025
R-4.3-win	OK	Mar 09 2025
R-4.3-mac	OK	Mar 09 2025

Exports:accuracy.kfold accuracy.MC accuracy.plot assign.kfold assign.matrix assign.MC assign.X check.loci compile.data membership.plot read.Genepop read.Structure reduce.allele

Dependencies:caret class cli clock codetools colorspace cpp11 data.table diagram digest doParallel dplyr e1071 fansi farver foreach future future.apply generics ggplot2 globals glue gower gtable hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix mgcv ModelMetrics munsell nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 randomForest RColorBrewer Rcpp recipes reshape2 rlang rpart scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tree tzdb utf8 vctrs viridisLite withr

Help page	Topics
Estimate assignment accuracies of K-fold cross-validation results	accuracy.kfold
Estimate assignment accuracy of Monte-Carlo cross-validation results	accuracy.MC
Make a boxplot (ggplot2 style) of assignment accuracy from cross-validation results	accuracy.plot
Population assignment test using K-fold cross-validation	assign.kfold
Make an assignment maxtrix from cross-validation results	assign.matrix
Population assignment test using Monte-Carlo cross-validation	assign.MC
Perform a population assignment test on unknown individuals using known data	assign.X
Check which loci frequently have high Fst across training sets	check.loci
Compile genetic and other non-genetic data	compile.data
Make a membership probability plot using results from K-fold cross-validation (ggplot2 style)	membership.plot
Read GENEPOP format file	read.Genepop
Read Structure format file	read.Structure
Remove low variance alleles (dimensionality reduction)	reduce.allele

Package: assignPOP 1.3.0

assignPOP: Population Assignment using Genetic, Non-Genetic or Integrated Data in a Machine Learning Framework

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)