I'm modelling habitat selection using boosted regression trees (BRTs), which I prefer over linear models for a variety of reasons (modeling complex nonlinear relationships and interactions, multicollinearity, etc.). I now have a dataset that includes repeated measures for individual animals (100s or 1000s per individual). In a linear modeling framework this is easily analyzed with random effects, but I haven't found any way to model clustered data with BRTs.
I found methods that produce individual regression trees (e.g., Mixed Effect Regression Trees (MERT), Regression Trees with Random Effects (REEMtree)). But the only mention I've found of random effects with BRTs is in Buston and Elith 2011 (http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2011.01803.x/abstract), who did a post-hoc residuals analysis to identify group effects. Is there any way to account for clustering within the BRT analysis itself? Thanks!