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Discriminant Analysis for Evolutionary Inference

0packages depend
3.1Kdownloads / year
0.0%test coverage
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Overview

About

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, doi:10.22541/au.159256808.83862168), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.

Install

Health

CRAN checks
13OK
Slowest check: 3.8 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-06-09
  • ERROR12 OK, 1 ERROR2026-06-08
  • OK13 OK2026-05-02
Show 2 earlier changesHide 2 earlier changes
  • ERROR11 OK, 1 ERROR2026-04-25
  • NOTE11 OK, 3 NOTE2026-03-10
Advisories

none known

Documentation

In release 1.2.0
READMEYes · 215 wordsVignettesYes · 1 · dynamicNEWSYes · 67% structured · mentions 1.2.0Citation fileNoWebsiteYes
Help pages
9
Examples that run
100%7 run in a plain check
Exported functions with examples
78%
Documented parameters
94%
Return-value docs
100%
Help pages citing references
89%

Downloads

3.1K
CRAN downloads in the past year
Rank #14,668 · ~9/day · ~260/mo
Daily download trend is not available in this view yet.
26030 days
82290 days
3.1K1 year
Compare downloads with other packages →
Also on78 r2u9 autocran

Repository

1Stars
0Forks
0Open issues
0Open PRs
2Releases
64Commits
1Commit identities
discriminant-analysiskernel-principle-componentsdapckernel-localpopulation-structure-inferenceecologicalkernelprincipal-components
License file reads as GPL-3.0 · 64 commits · Last activity 2021-07-11 · 0% stars, 30d
Responsiveness
not measured

Stars over time

2020-04-24 · 12026-07-07 · 1
PracticesBeta

1 development-tooling and community-health practice detected across 1 family in the upstream repository

Checks run against github.com/xinghuq/da on 2026-10-21.

Docs source (1)

GitHub Pages site last deployed 2021-06-10

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
9 external dependencies (excludes base and recommended)

Installing DA adds 114 packages (60 with compiled code, 102 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.

Depends (1)
R >= 3.5
Imports (10)
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (1)
Author, Maintainer since 1.2.0 (2021), Copyright holder
Releases (1)
  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • 1.2.0Latest
    2021-07-12 · current release
  • R
    R 4.1.0 released · 2021-05-18

Package metadata

First published
2021-07-12
Total releases
1 / 5 yrs
License
GPL-3 OSI
Minimum R
≥ 3.5
Download size
1.6 MB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("DA")
Qin, X. (2021). DA: Discriminant Analysis for Evolutionary Inference (Version 1.2.0) [Computer software]. https://doi.org/10.32614/CRAN.package.DA

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for DA version 1.2.0 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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