Distributional regression forests on arXiv

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Distributional regression trees and forests provide flexible data-driven probabilistic forecasts by blending distributional models (for location, scale, shape, and beyond) with regression trees and random forests. Accompanied by the R package disttree. Read more ›

BAMLSS paper published in JCGS

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Bayesian additive models for location, scale, and shape (and beyond) provide a general framework for distributional regression. Accompanied by the R package bamlss. Read more ›

GLMM trees published in BRM

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Generalized linear mixed-effects model trees, especially for detecting treatment-subgroup interactions in clustered data. Accompanied by the R package glmertree, combining partykit::glmtree and lme4::glmer. Read more ›

Welcome to the redesigned web page

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Redesigned the personal web page using a responsive jekyll design with a fresh theme and better-structured content. Read more ›