This repository has been closed and continues under a new repository with the name douconca (double constrained correspondence analysis). The names dcca,DCCA and dcCA refer to different methods and packages. The new name douconca is hopefully unique.

dcCA analyzes multi-trait multi-environment ecological data by double constrained correspondence analysis (ter Braak et al. 2018) using \code{vegan} and native R code. It has a \code{formula} interface for the trait- (column-) and environment- (row-) models, which allows to assess, for example, the importance of trait interactions in shaping ecological communities. 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 CWM regressions are specified with an environmental formula and the SNC regressions are specified with a trait formula. dcCA finds the environmental and trait gradients that optimize these regressions. The first step of the algorithm uses \code{\link[vegan]{cca}} to regress the (transposed) abundance data on to the traits and the second step uses \code{\link[vegan]{rda}} to regress the CWMs of the orthonormalized traits on to the environmental predictors. The abundance data are divided by the sample total (i.e. 'closed') in the vegan-based version. This has the advantage that this multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted, which may give a puzzling differences between common univarite and this multivariate analysis. Reference: ter Braak, CJF, Šmilauer P, and Dray S. 2018. Algorithms and biplots fordouble constrained correspondence analysis. Environmental and Ecological Statistics, 25(2), 171-197. https://doi.org/10.1007/s10651-017-0395-x

You can install the released version of dcCA from github by invoking the R-code within an R-console:

install.packages("remotes")
remotes::install_github("CajoterBraak/dcCA")