Tag: tree
Subgroup detection in linear growth curve models
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New arXiv working paper showing how generalized linear mixed effects model (GLMM) trees, along with their R implementation in the glmertree package, can be used to identify subgroups with differently shaped trajectories in linear growth curve models. Read more ›
Tree models for assessing covariate-dependent method agreement
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New arXiv working paper introducing conditional method agreement trees (COAT) which can capture the dependency of a Bland-Altman analysis on covariates. It is ccompanied by an R implementation in the CRAN package coat. Read more ›
Model-based causal forests for heterogeneous treatment effects
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A new arXiv paper investigates which building blocks of random forests, especially causal forests and model-based forests, make them work for heterogeneous treatment effect estimation, both in randomized trials and observational studies. Read more ›
Network trees: networktree 1.0.0, web page, and Psychometrika paper
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Version 1.0.0 (and actually 1.0.1) of the R package 'networktree' with tools for recursively partitioning covariance structures is now available from CRAN, accompanied by a paper in Psychometrika, and a dedicated software web page. Read more ›
Structural equation model trees with partykit and lavaan
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To capture heterogeneity in structural equation models (SEMs), the model-based recursive partitioning (MOB) algorithm from partykit can be coupled with SEM estimation from lavaan. Read more ›
Circular regression trees and forests
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A flexible framework for probabilistic forecasting of circular data is introduced, using distributional regression trees and random forests based on the von Mises distribution. Read more ›
Network model trees
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The effect of covariates on correlations in psychometric networks is assessed with either model-based recursive partitioning (MOB) or conditional inference trees (CTree). Read more ›
The power of unbiased recursive partitioning
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The significance tests underlying the unbiased tree algorithms CTree, MOB, and GUIDE are embedded into a unifying framework. This allows to assess relative strengths and weaknesses in a variety of setups, highlighting the advantages of score-based tests (as in CTree/MOB) vs. residual-based tests (as in GUIDE). Read more ›
Personalized treatment effects with model4you
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Personalized treatment effects can be estimated easily with model-based trees and model-based random forests using the R package model4you. Read more ›
Spatial lag model trees
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Economic growth models are recursively partitioned to assess heterogeneity in growth and convergence across EU regions while adjusting for spatial dependencies. Accompanied by R package lagsarlmtree, combining partykit::mob and spdep::lagsarlm. Read more ›