                 Changes in version 0.2-7 (2026-08-21)                  

  - ...

                 Changes in version 0.2-6 (2024-11-05)                  

  - Paper on linear growth curve modeling with lmertree() published:
    Fokkema M, Zeileis A (2024). "Subgroup Detection in Linear Growth
    Curve Models with Generalized Linear Mixed Model (GLMM) Trees."
    Behavior Research Methods, 56(7), 6759-6780.
    doi:10.3758/s13428-024-02389-1

  - Reduce precision in tests in order to avoid NOTEs in CRAN checks.

                 Changes in version 0.2-5 (2024-06-28)                  

  - Added functions cv.lmertree() and cv.glmertree(), implementing
    honest estimation of model coefficients per Athey & Imbens (2016)

  - Fixed bug in coef() method betamertree().

                 Changes in version 0.2-4 (2023-09-09)                  

  - Additional arguments to lattice::dotplot can now be passed to
    plot.lmertree()/plot.glmertree().

  - Dedicated plotting function for partitioning growth curves has been
    added, see which = "growth" in ?plot.lmertree and ?plot.glmertree.

                 Changes in version 0.2-3 (2023-03-22)                  

  - Minor bug fixes.

  - Experimental function betamertree() added, for partitioning
    mixed-effects beta regression trees.

                        Changes in version 0.2-1                        

  - Bug fix for missing data: Listwise deletion over all predictor,
    response, and splitting variables is now applied. Otherwise, global
    and local parts of (g)lmertree model could be estimated based on a
    different sample size or an error could occur.

  - Fixed bug: If dot . was used in formula for specifying partitioning
    variables, tree fitting would pick up tree structure from previous
    iteration. Fixed now.

  - Fixed bug: When type = "simple" in call to function plot.lmertree()
    and plot.glmertree() is specified, further arguments are now passed
    correctly.

                 Changes in version 0.2-0 (2019-11-19)                  

  - Bug fix in predict() method: Erroneous allocation of new
    observations to tree nodes could occur, which has been fixed now.

  - New plotting method for (g)lmertrees. Arguments which, type, and
    fitted now support wider range of plots. E.g., fitted argument
    provides different ways to compute fitted values in terminal panel
    plots. E.g., type argument now supports the plotting of
    fixed-effects coefficients with standard-error bars.

  - Two artificial example datasets added to illustate fitting of
    constant fits (MHserviceDemo) and growth-curve models
    (GrowthCurveDemo) in terminal nodes.

  - New sections added to vignette, illustrating how mixed-effects
    regression trees with constant fits and growth curve models in
    terminal nodes can be fitted.

                 Changes in version 0.1-2 (2019-02-20)                  

  - Functions glmertree() and lmertree() now take offset arguments.

  - Argument ranefstart of functions glmertree() and lmertree() can now
    be set to TRUE. As a result, the random effects will be estimated
    before fitting the tree in the first iteration. This may yield
    better results when random effects are expected to be substantial.

  - Functions glmertree() and lmertree() now take cluster argument, an
    optional vector with cluster IDs to be employed for clustered
    covariances in the parameter stability tests.

  - Bugs fixed: Argument dfsplit is now passed correctly to tree fitting
    functions, additional arguments are now passed correctly to lmer()
    and glmer().

  - Arguments lmer.control in lmertree() and glmer.control in
    glmertree() are now actually passed to lmer() and glmer()
    internally.

                 Changes in version 0.1-1 (2017-06-03)                  

  - First CRAN release of the glmertree package for fitting generalized
    linear mixed-effects model trees in R. For an introduction to the
    underlying methods see
    
    Fokkema, Smits, Zeileis, Hothorn, Kelderman (2015). Detecting
    Treatment-Subgroup Interactions in Clustered Data with Generalized
    Linear Mixed-Effects Model Trees. Working Paper 2015-10. Working
    Papers in Economics and Statistics, Research Platform Empirical and
    Experimental Economics, Universitaet Innsbruck. URL
    https://EconPapers.RePEc.org/RePEc:inn:wpaper:2015-10
    
    The package is under development on R-Forge at
    https://R-Forge.R-project.org/projects/partykit/
