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glmertree - Generalized Linear Mixed Model Trees

Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 'partykit'. The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>). For detecting and modeling subgroups in growth curves with GLMM trees see Fokkema & Zeileis (2024; <DOI:10.3758/s13428-024-02389-1>).

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6.44 score 3 dependents 51 scripts 5.0k downloads

mvs - Methods for High-Dimensional Multi-View Learning

Methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework. For technical details on the MVS and stacked penalized logistic regression (StaPLR) methods see Van Loon, Fokkema, Szabo, & De Rooij (2020) <doi:10.1016/j.inffus.2020.03.007> and Van Loon et al. (2022) <doi:10.3389/fnins.2022.830630>.

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2.70 score 4 scripts 171 downloads

gamtree - GAM-based Recursive Partitioning

Recursively partitions the observations in a dataset, based on the parameters of a generalized additive model (GAM). Function splinetree implements partitioning based on parametric or non-penalized spline models. It offers faster computational speed and more flexibility for modeling multilevel and longitudinal data. Function gamtree implements partitioning based on semi-parametric or penalized spline models, a.k.a. smoothing splines. It requires less user involvement in correctly specifying the splines and knots, but has a heavier computational load and offers less flexibility in modeling multilevel and longitudinal data. Packages mgcv and splines are used for spline and model estimation, packages partykit and merDeriv are used for partitioning and derivative computations.

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2.40 score 5 stars

curtail - A package for test curtailment

A package for creating and assessing the performance of curtailed tests. A curtailed test is a variable-length test, which allows for early stopping of item administration when further items are unlikely or unable to change the final (classification) decision. The package allows for creating and assessing deterministically and stochastically (based on empirical proportions) curtailed tests. In future versions of the package, model-based stochastic curtailment may be added. The package allows for test curtailment through the creation of look-up tables, providing item-specific cut-off values for early stopping of item administration. In addition, the package allows for assessing efficiency (number of items administered to arrive at a final decision) and accuracy (concordance between the final decision based on curtailed and full-length test).

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1.70 score