# `CAST` 1.1.0
* new features:
  * knndm for train-test splits
  * ffs allows for searching the whole range of predictors (earlyStopping=FALSE) 
* bug fix: 
  * sampling of prediction points in geodist no longer requires reprojection and transformation of raster modeldomains by default (issue [#130](https://github.com/HannaMeyer/CAST/issues/130))
  * now allows for tibble in AOA
* modifications: 
  * geodist was re-structured, and some parameters have been re-named ('cvtrain' to 'CVtrain', 'cvfolds' to 'CVtest', 'type' to 'dist_space', 'timevar' to 'time_var') or newly introduced ('dist_fun', 'scale_vars')
  * knndm was re-structured to a lesser extent, the 'space' parameter has been re-named to 'dist_space', 'useMD' was dropped and replaced by 'dist_fun', 'scale_vars' was newly introduced
  * AOA as a generic
* other improvements:
  * several new tests
  * added code coverage
  * improved documentation
  * unify plotting style
  * removed dependencies
  * workflow figure improved

# `CAST` 1.0.4
* bug fix: formatting issues in tests
* references updated

# `CAST` 1.0.3
* bug fix: default algorithm for FNN functions changed

# `CAST` 1.0.2
* bug fix: tests run conditionally

# `CAST` 1.0.1
* bug fix: fix failed tests in global_validation

# `CAST` 1.0.0
* new features:
  * calculate local point density within AOA
  * option of spatial error profiles (errorProfiles with variable="geodist")
  * normalize_DI for a more intuitive interpretation
  * geodist allows calculating temporal distances
  * ffs now can be run in parallel (Linux only)
  * vignette "Cross-validation methods in CAST"
  * knndm in feature space (experimental)
  * nndm in feature space (experimental)
* modifications:
  * function DItoErrormetric renamed to errorProfiles and allows for other dissimilarity measures
  * Improvement and homogenization of plotting methods for nndm, knndm and geodist objects
  * aoa and trainDI `weight` now allows list input
  * vignette on Introduction to CAST updated
* deprecated:
  *plot_geodist (replaced by plot.geodist)
  *plot_ffs (replaced by plot.ffs)
  *calibrate_aoa (replaced by errorProfiles)
  
# `CAST` 0.9.0
* new features:
  * CAST functions now return classes with generic plotting and printing
  * new dataset for examples, tutorials and testing: data(splotdata)
* modifications:
  * calibrate_aoa is now DItoErrormetric and returns a model (see function documentation)
  * plot_geodist is now geodist. The result can be visualized with plot()
  * plot_ffs is now plot(ffs)
* bug fix:
  * fix issue #65 (threshold)
* deprecated (soon):
  * plot_geodist, plot_ffs, calibrate_aoa
  
  
# `CAST` 0.8.1
* bugfix:
  * failed checks on Fedora 34 fixed
  
# `CAST` 0.8.0
* new features:
  * knndm as an alternative to nndm for large training data
* modifications:
  * transition from raster to terra

# `CAST` 0.7.1
* new features:
  * Mahalanobis distance for AOA assessment as option
* modifications:
  * faster estimation of the AOA
  * parallel option for AOA deprecated (see vignette)
    * delineation of the default threshold fixed as suggested in github.com/HannaMeyer/CAST/issues/46
* bugfix:
  * fixed issue github.com/ropensci/rnaturalearth/issues/69
  

# `CAST` 0.7.0
* new feature: 
  * nndm cross-validation as suggested by Milà et al. (2022)
* modifications
  * plot_geodist works with NNDM
  * trainDI works with NNDM
  * rename of parameter folds in AOA and trainDI

# `CAST` 0.6.0
* new feature: 
  * trainDI allows to calculate the DI of the training dataset separately from the aoa function
  * plot and print functions for the AOA
  * function to plot nearest neighbor distance distributions in geographic and feature space
  * function global_validation added
* modifications
  * extensive restructuring of the AOA function
  * ffs and bss can be used with global_validation
* bugfix:
  * error in manual assignment of weights fixed

# `CAST` 0.5.1
* resolved dependence on package "GSIF" which was removed from the CRAN repository  

# `CAST` 0.5.0
* new feature: 
  * AOA can run in parallel
  * calibration of the DI (calibrate_aoa)
* bugfix:
  * aoa will work now with large training sets
* modifications:
  * default threshold of AOA changed

# `CAST` 0.4.2
* new feature:
  * aoa now working with categorical variables
* bugfix:
  * fixed error in ffs when >170 variables are used
* minor changes:
  * changed order of parameters in aoa
  * tutorial "Introduction to CAST" improved

# `CAST` 0.4.1
* new feature:
  * vignette: tutorial introducing the "area of applicability"
  * variable threshold for aoa
  * various modifications in aoa in line with submitted paper
  
# `CAST` 0.4.0
* new feature: 
    * new function "aoa": quantify and visualize the area of applicability of spatial prediction models
    * "minVar" in ffs: Instead of always starting with 2-pair combinations, ffs can now also be started with combinations of more variables (e.g starting with all combinations of 3)
* bugfix:
  * ffs failed for "svmLinear" in previous version because of S4 class issues. Fixed now.

# `CAST` 0.3.1

* bugfix: 
  * CreateSpaceTimeFolds accepts tibbles
  * CreateSpaceTimeFolds automatically reduces k if necessary
  * ffs accepts further arguments taken by caret::train
* new feature: plot_ffs has option to plot selected variables only

# `CAST` 0.3.0

* new feature: Best subset selection (bss) with target-oriented validation as (very slow but very reliable) alternative to ffs

* minor adaptations: verbose option included, improved examples for ffs

* bugfix: minor adaptations done for usage with plsr

# `CAST` 0.2.1

* new feature: Introduction to CAST is included as a vignette.

* bugfix: minor error fixed in using user defined metrics for model selection.

# `CAST` 0.2.0

* bugfix: `ffs` with option withinSE=TRUE did not choose a model as "best model" if it was within the SE of a model that was trained in an earlier run but had the same number of variables. This bug is fixed and if withinSE=TRUE ffs now only compares the performance to models that use less variables (e.g. if a model using 5 variables is better than a model using 4 variables but still in the SE of the 4-variable model, then the 4-variable model is rated as the better model).

* new feature: `plot_ffs` plots the results of ffs to visualize how the performance changes according to model run and the number of variables being used.

# `CAST` 0.1.0

Initial public version on CRAN

