douconca 1.2.2

New coef.dcca() and fitted.dcca() functions with predict.dcca() adapted. The function coef() can give fourth-corner correlations and regression coefficients.
Patch release with extended test files and associated small corrections, for example, SDS (standard deviation of predictors) was in v1.2.1 a constant factor too large with the default of the argument divideBySiteTotals (the regression weights and t-values were correct).

douconca 1.2.1

The package can now do general dc-CA, instead of the vegan-based version with equal site weights only. For users of the previous version, the function dc_CA_vegan has been replaced by the more general function dc_CA. The default gives the same analysis. By specifying the argument divideBySiteTotals = FALSE, obtain the original dc-CA analysis with unequal site weights.
The plot_dcCA function is now a method: plot.
General dc-CA required weighted redundancy analysis. For this, a new function wrda has been added, with methods for print, scores and anova.
A predict function has been added.
A dc-CA can be computed from community-weighted means (CWMs) with trait and environment data with species and site weights. See the new function fCWM_SNC. This is of interest, for example, to make a dc-CA analysis reproducible when the abundance data cannot be made public, and it may also allow to perform dcCA with intra-species trait variation. The user needs to be able to compute meaningful CWMs in this case and supply trait data that reflect the (species-weighted) inter-trait covariance.
Several functions are updated. In particular, there are corrections to the anova function.

The scores.dccav function is corrected concerning intra-set correlations for traits and environmental variables.
The plotting functions are updated to avoid ggplot2 warnings on color and size.
The fitted straight lines in the plots use the implicit weights (they did already, but the help said they did not).