R package for partitioning generalized linear mixed-effects regression (glmer) models. Available from CRAN (stable release) and R-Forge (development version). For more info, see Fokkema et al. (2018; Behavior Research Methods) for a more technical explanation, Fokkema et al. (2021; Psychotherapy Research) for an introduction aimed at researchers in psychology, or check out the tutorial by typing in R:

    install.packages("glmertree") ## stable release from CRAN

    install.packages("glmertree", repos="") ## devel version

    vignette("glmertree", "glmertree")


An R package for fitting prediction rule ensembles. It implements the method of Friedman and Popescu (2008) with several improvement. E.g., it employs an unbiased algorithms for rule induction; it supports continuous, binary, multinomial, count, multivariate and survival responses; it provides more control over sparsity of the final ensemble; it allows for plotting the ensemble. The package is available from CRAN (stable release) and GitHub (development version). For more info, see Fokkema (2020; Journal of Statistical Software)  or Fokkema & Strobl (2020; Psychological Methods  or arXiv version for free access). To install and use the package in R:

    install.packages("pre") ## stable release from CRAN


  install_github("marjoleinF/pre") ## devel version


 A general introduction to the package's functionality is provided here: Short tutorials on parameter tuning, missing data handling and obtaining simpler rule ensembles are provides as 'vignettes' here:


curtail: An R package for test curtailment. A curtailed test is a variable-length test, allowing for early stopping of item administration when further items are unlikely or unable to change the final (classification) decision. The package supports deterministic and stochastic (based on empirical proportions) curtailment. It is available on GitHub. The package can be installed and loaded in R as follows:




A short tutorial is provided here. For more info, see both Fokkema et al. (2014) papers on curtailment or De Beurs et al. (2016) under Publications.