Changes in version 1.2.0 (2023-10-24)                  

  - added new calculation_method for surv_shap() called "treeshap" that
    uses the treeshap package (#75)
  - enable to calculate SurvSHAP(t) explanations based on subsample of
    the explainer's data
  - changed default kernel width in SurvLIME from sqrt(p * 0.75) to
    sqrt(p) * 0.75
  - fixed error in SurvLIME when non-factor categorical_variables were
    provided

                 Changes in version 1.1.3 (2023-09-06)                  

  - fixed not being able to plot or print SurvLIME results for the cph
    model sometimes. (#72)
  - added global explanations via the SurvSHAP(t) method (see
    model_survshap() function)
  - added plots for global SurvSHAP(t) explanations (see
    plot.aggregated_surv_shap())
  - added Accumulated Local Effects (ALE) explanations (see
    model_profile(..., type = "accumulated"))
  - added 2-dimensional PDP and ALE plots (see model_profile_2d()
    function)
  - added plot(..., geom="variable") function for plotting PDP and ALE
    explanations without the time dimension
  - new explainers: for flexsurv models and for Python scikit-survival
    models (can be used with reticulate)
  - new plot type for model_survshap() - curves (with functional box
    plot)
  - added diagnostic explanations - residual analysis (see
    model_diagnostics() function)
  - added new times generation method "survival_quantiles" and setting
    it as default (see explain())
  - made improvements on the vignettes for the package (see
    vignette("pdp") and vignette("global-survshap"))
  - increased the test coverage of the package
  - reduced the number of expensive requireNamespace() calls (#83)

                 Changes in version 1.0.0 (2023-03-20)                  

  - breaking change: refactored the structure of
    model_performance_survival object - calculated metrics are now in a
    $result list.
  - added new calculation_method for surv_shap() called "kernelshap"
    that use kernelshap package and its implementation of improved
    Kernel SHAP (set as default) (#45)
  - rename old method "kernel" to "exact_kernel"
  - added new import (kernelshap package)
  - fixed invalid color palette order in plot feature importance
  - fixed predict_parts survshap running out of memory with more than 16
    variables (#25)
  - added max_vars parameter for predict_parts explanations (#27)
  - set max_vars to 7 for every method
  - refactored survshap code (#29, #30, #43)
  - fixed survshap error when target columns named different than time
    and status (#44)
  - fixed survlime error when all variables are categorical (#46)
  - fixed subtitles in feature importance plots (#11)
  - added the possibility to set themes with set_theme_survex() (#32)
  - added the possibility of plotting multiple predict_parts() and
    model_parts() explanations in one plot (#12)
  - fixed the x axis of plots (it now starts from 0) (#37)
  - added geom_rug() to all time-dependent plots, marking event and
    censoring times (#35)
  - refactored surv_feature_importance.R - change auxiliary columns to
    include _ in their name. Necessary changes also done to plotting and
    printing functions. (#28)
  - changed default type argument of model_parts() to "difference" (#33)
  - refactored integration of metrics (#31)
  - changed behaviour of categorical_variables argument in model_parts()
    and predict_parts(). If it contains variable names not present in
    the variables argument, they will be added at the end. (#39)
  - added ROC AUC calculation and plotting for selected timepoints in
    model_performance() (#22)
  - added explanation_label parameter to predict_parts() function that
    can overwrite explainer label and thus, enable plotting multiple
    local SurvSHAP(t) explanations. (#47)
  - improved the printing of the explainer (#36)
  - reduced the default number of time points for evaluation when
    creating the explainer to 50

                 Changes in version 0.2.2 (2022-11-30)                  

  - improved and unified API documentation (#2)
  - added references to used methods (#5)
  - changed the package used to draw complex plots from gridExtra to
    patchwork (#7)
  - fixed subtitles in plots (#11)
  - fixed calculating of ROC curves for classification problems (#17)
  - added wrapper function for measures provided by mlr3proba (#10)
  - created vignette showing how to use mlr3proba with survex
  - fixed incompatibility with new ggplot2 version 3.4
  - added function for creating integrated versions of time-dependent
    metrics (#9)
  - move ingredients from imports to suggests

                 Changes in version 0.1.1 (2022-09-05)                  

  - The survex package is now public
  - model_parts, model_profile, predict_parts, predict_profile
    explanations implemented
  - C/D AUC, Brier score and (Harrell's) concordance index performance
    measures implemented
  - Explain methods for survival, ranger, randomForestSRC, censored and
    mlr3proba packages.