# `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

