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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.3.1 |
rolling linux/jammy R-4.5 | assignPOP_1.3.1.tar.gz |
259.4 KiB |
1.3.1 |
rolling linux/noble R-4.5 | assignPOP_1.3.1.tar.gz |
259.2 KiB |
1.3.1 |
rolling source/ R- | assignPOP_1.3.1.tar.gz |
106.4 KiB |
1.3.1 |
latest linux/jammy R-4.5 | assignPOP_1.3.1.tar.gz |
259.4 KiB |
1.3.1 |
latest linux/noble R-4.5 | assignPOP_1.3.1.tar.gz |
259.2 KiB |
1.3.1 |
latest source/ R- | assignPOP_1.3.1.tar.gz |
106.4 KiB |
1.3.1 |
2026-04-26 source/ R- | assignPOP_1.3.1.tar.gz |
106.4 KiB |
1.3.1 |
2026-04-23 source/ R- | assignPOP_1.3.1.tar.gz |
106.4 KiB |
1.3.1 |
2026-04-09 windows/windows R-4.5 | assignPOP_1.3.1.zip |
256.4 KiB |
1.3.0 |
2025-04-20 source/ R- | assignPOP_1.3.0.tar.gz |
105.8 KiB |