douconca: Double Constrained Correspondence Analysis for Trait-Environment
Analysis in Ecology
Double constrained correspondence analysis (dc-CA) analyzes
(multi-)trait (multi-)environment ecological data by using the 'vegan'
package and native R code. Throughout the two step algorithm of ter Braak
et al. (2018) is used. This algorithm combines and extends community-
(sample-) and species-level analyses, i.e. the usual community weighted
means (CWM)-based regression analysis and the species-level analysis of
species-niche centroids (SNC)-based regression analysis. The two steps use
canonical correspondence analysis to regress the abundance data on to the
traits and (weighted) redundancy analysis to regress the CWM of the
orthonormalized traits on to the environmental predictors. The function
dc_CA() has an option to divide the abundance data of a site by the site
total, giving equal site weights. This division has the advantage that the
multivariate analysis corresponds with an unweighted (multi-trait)
community-level analysis, instead of being weighted. The first step of
the algorithm uses vegan::cca(). The second step uses wrda() but
vegan::rda() if the site weights are equal. This version has a predict()
function. For details see ter Braak et al. 2018
<doi:10.1007/s10651-017-0395-x>.
and ter Braak & van Rossum 2025 <doi:10.1016/j.ecoinf.2025.103143>.
| Version: |
1.2.5 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
ggplot2 (≥ 3.5.1), ggrepel, gridExtra, permute, rlang, stats, vegan (≥ 2.6-8) |
| Suggests: |
rmarkdown, knitr, tinytest |
| Published: |
2026-02-23 |
| DOI: |
10.32614/CRAN.package.douconca |
| Author: |
Cajo J.F ter Braak
 [aut],
Bart-Jan van Rossum
[aut, cre] |
| Maintainer: |
Bart-Jan van Rossum <bart-jan.vanrossum at wur.nl> |
| BugReports: |
https://github.com/Biometris/douconca/issues |
| License: |
GPL-3 |
| URL: |
https://zenodo.org/records/13970152,
https://github.com/Biometris/douconca |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
douconca results |
Documentation:
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