Implementation of the double/debiased machine learning framework of
    Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear
    regression models, partially linear instrumental variable regression models,
    interactive regression models and interactive instrumental variable
    regression models. 'DoubleML' allows estimation of the nuisance parts in
    these models by machine learning methods and computation of the Neyman
    orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the
    'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on
    the 'R6' package is very flexible. More information available in the
    publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.

Overload utils::'?' to build unary and binary operators from existing functions, 
    piping operators of different precedence, and flexible syntaxes.

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>.