Changes in Version 1.0.6 (2023-02-13)

  - No major changes.


Changes in Version 1.0.5 (2022-06-10)

  - No major changes, several bug fixes, dependence on package akima 
      eliminated.




Changes in Version 1.0.4 (2022-03-30)

  - Implemented relaxed lasso fits and added vignette on relaxed fits, 
      see vignette("relaxed", "pre").

  - More informative warning message if no rules could be derived.




Changes in Version 1.0.3 (2022-02-24)

  - No news, update to fix failing test.




Changes in Version 1.0.2 (2022-02-24)

  - Added vignette explaining how to tune parameters of function pre() using
      package caret.

  - Added vignette on how to deal with missing values.

  - Added support for supplying a tibble instead of a data.frame to function pre().

  - Function importance is now an S3method.

  - Argument cex.axis of function (method) importance is now passed correctly.

  - Argument weights of functions pre() and gpe() now correctly passed to rule 
      induction functions.

  - Added progress bar indicating tree fitting progress.

  - Argument singleconditions implemented in function pre() (experimental).




Changes in Version 1.0.1 (2021-07-04)

  - Added function rare_level_sampler to deal with errors related to
      new factor levels.




Changes in Version 1.0.0 (2020-05-03)

  - Minor changes for compatibility with changed data.frame function 
      in R version 4.0.0.

  - References to paper in Journal of Statistical Software included.




Changes in Version 0.7.2 (2019-11-11)

  - Only minor internal changes, required for compatibility with new
      version of glmnet package.




Changes in Version 0.7.1 (2019-04-24)

  - Bugs fixed in caret_pre_model: tuning of penalty.par.val argument
      always yielded results for "lambda.1se" only. Results are now
      correctly returned for "lambda.min" and "lambda.1se". caret's 
      varImp() and predictors() not supported (perhaps temporarily), 
      as these would always employ default penalty.par.val of
      "lambda.1se". 

  - Bugs fixed in explain(). 




Changes in Version 0.7 (2019-03-30)

  - Added support for sparse rule matrix, which can be invoked through sparse 
      argument in pre(). If sparse = TRUE, memory usage will be reduced and 
      computation speed may be improved for large datasets.
 
  - Added function explain(), which provides (graphical) explanations of the 
      ensemble's predictions at the individual observation level.




Changes in Version 0.6 (2018-08-03)

  - Added support for survival responses (i.e., family = "cox") in pre() 

  - Added summary methods for pre and gpe. 

  - Extended support to all response variable types available in pre() for 
      functions plot(), importance() and cvpre().
 
  - plot.pre now allows for specifying separate plotting colors for rules 
      with positive and negative coefficients.

  - coef and print methods for pre now return descriptions for the intercept 
      (and factor variables), thanks to suggestion by Stephen Milborrow.

  - Bug fix in pre(): ordered factors no longer yield error. Implemented 
      new argument 'ordinal' in pre(), which specifies how ordered factors 
      should be processed.

  - Bug fix in cvpre(): pclass argument now processed correctly.

  - Bug fix in cvpre(): previously, SDs insteas of SEs were returned for 
      binary classification. Accurate standard errors are returned now.

  - Bugs fixed in coef.pre(), print.pre(), plot.pre() and importance() 
      when tree.unbiased = FALSE, thanks to a bug report by Stephen 
      Milborrow. 
 



Changes in Version 0.5 (2018-05-07)

  - Function pre() now also supports multinomial and multivariate 
      gaussian response variables.
  
  - Function pre() now has argument 'tree.unbiased'; if set to FALSE, 
      the CART algorithm (as implemented in package 'rpart') is employed 
      for rule induction.
      
  - Argument 'maxdepth' of function pre() allows for specifying 
      varying maximum depth across trees, through specifying a 
      vector of length ntrees, or a random number generating function.
      See ?maxdepth.sampler for details.




Changes in Version 0.4 (2017-08-31)

  - Added dataset 'carrillo'
  
  - By default, a gradient boosting approach is now taken for all response 
      types. That is, partykit::ctree() and a learning rate of .01 is 
      employed by default. Alternatively, glmtree() can be employed for tree 
      induction by sprecifying use.grad = FALSE.
      
  - The 'family' argument in pre() now takes character strings as well as glm 
      family objects. 
      
  - Functions pairplot() and interact() now use HCL instead of highly saturated
      HSV colors as default plotting colors.
      
  - Bug fixed in plot.pre: Node directions are now in accordance with 
      rule definition.

  - Bug fixed in predict.pre: No error printed when response variable is not 
      supplied.




Changes in Version 0.3 (2017-08-03):

  - Function gpe() added, which fits general prediction ensembles. By default,
      it fits an ensemble of rules, linear and hinge functions. Function gpe()
      allows for specifying custom baselearner generating functions and a custom
      fitting function for the final model.
  
  - Numerous bugs fixed, yielding faster computation times and clearer plots 
      with more customization options. 
      
  - Added support for count responses. Function pre() now has a 'family' 
      argument, which should be set to 'poisson' for count outcomes (the
      'family' argument is set automatically to 'gaussian' for numeric response
      variables and to 'binomial' for binary response variables (factors)).

  - A gradient boosting approach for binary outcomes is applied, by default,
      substantially reducing computation times. This can be turned off through 
      the 'use.grad' argument in function pre().

  - The default of the 'learnrate' argument of function pre() has been changed 
      to .01, by default. Before, it was .01 for continuous outcomes, but 0 for
      binary outcomes, to reduce computation time. With gradient boosting
      implemented, computation time is much reduced.
  
  - Argument 'tree.control' in function pre() allows for passing arguments to
      partykit tree fitting functions. 
        
  - Arguments for the cv.glmnet() function are directly passed through better 
      use of ... . Most importantly, this means that argument 'mod.sel.crit' 
      cannot be used anymore and should be referred to as 'type.measure' 
      (which will be directly passed to cv.glmnet). Similarly, 'thres' and 
      'standardize' are not explicit arguments of function pre() anymore and 
      can now be directly passed to cv.glmnet() using ... .
      
  - Better use of sample weights: weights specified with the 'weights' argument 
      in pre() are now used as weights in the subsampling procedure, instead of
      as observation weights in the tree-fitting procedure.
      
  - Added corplot() function, which shows the correlation between the 
      baselearners in the ensemble.

  - Function pairplot() returns a heatmap by default, a 3D or contour plot can
      also be requested.
  
  - Appearance of plot resulting from interaction() improved.
  



Changes in Version 0.2 (2017-04-25):

  - Added print() and plot() method for objects of class pre.
  
  - Added support for using functions like factor() and log() in formula 
      statement of function pre(). (thanks to Bill Venables for suggesting this)
  
  - Added support for parallel computating in functions pre(), cvpre(), 
      bsnullinteract() and interact(). 
  
  - Winsorizing points used for the linear terms are reported in the description 
      of the base learners returned by coef() and importance(). (Thanks to
       Rishi Sadhir for suggesting this)
  
  - Added README file.
  
  - Legend included in plot for interaction test statistics.
  
  - Fixed importance() function to allow for selecting final ensemble with 
	different value than 'lambda.1se'.
  
  - Cleaned up all occurrences of set.seed()
  
  - Fixed cvpre() function: penalty.par.val argument now included
  
  - Many minor bug fixes.




Changes in Version 0.1 (2016-12-23):

  - First CRAN release.
