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Changelog

Source: NEWS.md

FFTrees 2.1.0

FFTrees version 2.1.0 was released on CRAN [on 2025-09-03]. This version reduces non-essential functionality, increases robustness, and fixes some bugs.

Breaking changes

  • Deprecated all applications of non-FFT competitive algorithms (i.e., LR, SVM, RF) in FFT creation. From now on, consider using dedicated packages (such as parsnip or tidymodels) to create and evaluate these models.

Major changes

  • Added support for handling NA values.

Minor changes

Plotting:

  • plot.FFTrees() now labels row of 2x2 confusion matrix as “Prediction” when using “test” data.
  • plot.FFTrees() now has a truth.labels argument which, if set, distinguishes labels of true (signal vs. noise) cases from decision outcomes.
  • plot.FFTrees() now has a grayscale argument which, if TRUE, creates a gray scale plot.

Decision costs:

  • Increased value of cost_cues_default from 0 to 1, so that default cue costs correspond to mcu.

Details

  • Fixed bug for missing plot title.
  • Added @aliases FFTrees-package to documentation of main FFTrees() function.
  • Removed redundant data_old folder.

The current development version of FFTrees is available at https://github.com/ndphillips/FFTrees.

FFTrees 2.0.0

CRAN release: 2023-06-05

FFTrees version 2.0.0 was released on CRAN [on 2023-06-06]. This version adds functionality, improves consistency, and increases robustness.

Changes since last release:

Major changes

  • Enabled conversions between tree definition formats and manipulating FFT definitions:
    • tree definition and conversion functions: get_fft_df, read_fft_df, write_fft_df, add_fft_df
    • tree trimming functions: add_nodes, drop_nodes, edit_nodes, flip_exits, reorder_nodes, select_nodes
  • Growing FFTs:
    • enabled stopping.rule = "statdelta"
    • fixed a bug in fftrees_grow_fan() that prevented ifan algorithm from stopping when finding a perfect FFT (given the current goal.chase parameter)
  • Handling missing inputs (NA values) in data:
    • NA values in categorical (i.e., character/factor/logical) predictors are treated as <NA> factor levels
    • NA values in numeric predictors are either ignored (by default) or imputed (as the mean of the corresponding predictor) when creating and using FFTs to decide/predict (if possible)
    • NA values in the criterion variable are yet to be dealt with

Minor changes

  • Added utility functions (and corresponding verification functions):
    • get_best_tree() retrieves the ID of the best tree in an FFTrees object (given goal)
    • get_exit_type() converts a vector of exit descriptions into FFT exits (given exit_types)
    • get_fft_df() retrieves the tree definitions of an FFTrees object
  • Added cost information when printing FFTs (with print.FFTrees()).
  • Improved user feedback (by making quiet a list with four options).
  • Increased vocabulary for interpreting verbal FFT descriptions (using my.tree).
  • Improved documentation of included data (e.g., in FFTrees.guide()).

Details

  • Added global constants and utility functions.
  • Added progress bar of cli package (removing dependency on progress).
  • Added exit_types as global constant.
  • Improved data cleaning (consistent for training and test data).
  • Revised documentation, vignettes, and tests.

FFTrees 1.9

1.9.0

FFTrees version 1.9.0 was released on CRAN [on 2023-02-08]. Apart from adding functionality and fixing minor bugs, this version improves consistency, robustness, and transparency.

Changes since last release:

Major changes

  • Enabled optimizing for a user-defined my.goal on cue and tree levels (as defined by my.goal.fun).
  • Enabled optimizing for dprime on cue and tree levels (by using "dprime" as goal.threshold, goal.chase, or goal values).
  • Increased vocabulary for interpreting verbal FFT descriptions (using my.tree).
  • Improved summary.FFTrees() function:
    • Included current goal and cost values (if "cost" occurs in goals).
    • Included criterion base rates (in performance statistics on train and test data).

Minor changes

  • Included dprime values in cue level statistics (x$cues$thresholds and x$cues$stats).
  • Included dprime values in competition statistics (x$competition$train and x$competition$test).
  • Improved user feedback on combinations of goal and cost values.
  • Prepared for modular tree translation and editing functions (util_gfft.R).
  • Prepared for global tree notation separator (fft_node_sep).
  • Added decision outcome and cue costs to asif_results (in fftrees_grow_fan()).

Details

  • Added verification functions (for checking integrity of objects and validity of inputs).
  • Deprecated the rounding argument of FFTrees().
  • Re-arranged arguments of key functions (FFTrees() and fftrees_create()) by functionality.
  • Re-arranged and cleaned code (in main and helper functions).
  • Re-defined local constants as global constants (in util_const.R).
  • Revised status badges in README.
  • Tweaked plotting parameters.
  • Fixed bugs and revised vignettes.

FFTrees 1.8

1.8.0

FFTrees version 1.8.0 was released on CRAN [on 2023-01-06]. This version mostly extends and improves existing functionality.

Changes since last release:

Major changes

  • Enabled manually defining FFTs with tree.definitions or using FFTs of object in FFTrees().
  • Enabled setting goal = 'dprime' to select FFTs in FFTrees().
  • Added and improved user feedback (when quiet = FALSE).

Minor changes

  • Plotting FFTs with plot.FFTrees():
    • Show n.per.icon legend when what = 'icontree'.
    • Bug fix: Removed clipping of titles and labels.
    • Tweaked spacing parameters.

  • Trimmed white space from elements in tree definitions (in fftrees_apply.R).

  • Added check that cues occur in current data (in verify_all_cues_in_data()).

Details

  • Removed anova from stats imports.
  • Replaced expect_is() by more precise testthat inheritance functions.
  • Replaced crayon package by cli package.
  • Revised documentation and vignettes.
  • Fixed bugs and revised code to increase robustness.

FFTrees 1.7

1.7.5

FFTrees version 1.7.5 was released on CRAN [on 2022-09-15]. This version contains mostly bug fixes, but also improves and revises existing functionality.

Changes since last release:

Major changes

  • Added distinctions between FFTs that “decide” vs. “predict” by using corresponding labels in plots and verbal descriptions.

  • Improved plotting and printing FFTs (with plot.FFTrees() and print.FFTrees()):

    • Added new plotting options (e.g., what = 'all' vs. what = 'tree' and what = 'icontree').
    • Added distinction in header of icon guide between FFTs that “decide” (for training data) vs. “predict” (for test data).
    • Enabled applying a tree to new test data when providing a data frame as data.
    • Enabled passing some graphical parameters (e.g., col, font, adj) to text of panel titles.
    • Return an invisible FFTrees object x (to allow re-assigning to global x when using new test data).

Minor changes

  • Added wacc to measures computed for competing algorithms.

  • Plotting with plot.FFTrees():

    • Adjusted space for title to width of main argument.
    • Deprecated the stats argument.
    • Moved utility functions to helper_plot.R.

Details

  • Revised documentation and vignettes.
  • Renamed internal functions and variables.

1.7.0

FFTrees version 1.7.0 was released on CRAN [on 2022-08-31]. This version contains numerous bug fixes and improves or revises existing functionality.

Changes since last release:

Major changes

  • Improved functionality of print.FFTrees():
    • Added data argument to print an FFT’s training performance (by default) or prediction performance (when test data is available).
    • Enabled setting tree to "best.train" or "best.test" (as when plotting FFTs).
    • Reporting bacc or wacc in Accuracy section (and sens.w, if deviating from the default of 0.50).
    • Improved readability of 2x2 confusion table (by right-justifying digits).
    • Moved cost information from Accuracy to Speed, Frugality, and Cost section.
  • Fixed bugs and improved functionality of plot.FFTrees():
    • Improved plot for what = 'ROC' analogous to what = 'cues'.
    • Reporting bacc or wacc in Accuracy section (and sens.w value, if deviating from the default of 0.50).
    • Fixed bug to re-enable setting tree to "best.train" or "best.test".
    • Fixed bug to show correct point labels in ROC curve panel.
  • Fixed bugs and improved functionality of showcues():
    • Using current goal of object x as cue ranking criterion (rather than always using wacc).
    • Subtitle now shows sens.w value when goal == 'wacc'.
    • Cue legend now accommodates 0 < top < 10.
    • Removed redundant data argument (as FFTrees objects only contain cue training data).
    • Added alt.goal argument (to allow ranking cue accuracies by alternative goals).
    • Added quiet argument (to hide feedback messages).
    • Added subtitle (to signal current cue accuracy ranking criterion).
  • Improved version of summary.FFTrees():
    • Print tree performance summary and goal information (on the console).
    • Return tree definitions and stats (as a list).
  • Fixed a bug that forced reversals of final exits in the final node when manually creating FFTs with my.tree or fftrees_wordstofftrees().

Minor changes

  • Changed tree statistics for test data from data frames to tibbles.
  • Improved feedback on missing decision labels when creating FFTs from descriptions with my.tree or fftrees_wordstofftrees().
  • Deprecated the store.data argument of FFTrees().

Details

  • Changed primary package maintainer to Hansjoerg Neth, but Nathaniel Phillips is still on board.
  • Revised text, examples, and links in vignettes.
  • Reduced clutter by recycling code and combining files.
  • Cleanup of code and documentation.

FFTrees 1.6

FFTrees version 1.6.6 was released on CRAN [on 2022-07-18].

Changes since last release:

1.6.6

1.6.5

  • Cleanup to get package back on CRAN after failed submission on July 17, 2022.

1.6.4

  • Cleanup to get package back on CRAN after failed submission on July 16, 2022.

1.6.3

  • Additional cleanup to get package back on CRAN after failed submission on July 12, 2022.

1.6.2

  • Internal cleanup to get package back on CRAN.

1.6.1

  • plot.FFTrees() no longer saves graphic params changed in par().
  • plot.FFTRrees(): When test = 'best.test' and no test data are provided, the information text is no returned with message() rather than print().
  • Deprecation notes of plot.FFTrees() are now returned as warnings, not messages.

FFTrees 1.5

1.5.7

  • Officially deprecated the "max" and "zigzag" algorithms.
  • Minor cleanup throughout.

1.5.0

  • Fixed warnings for CRAN submission.
  • FFTrees objects now have a nicer internal structure.
  • Added tests throughout (finally).
  • Extensive code cleanup, which should speed things up.
  • New print method includes an ASCII confusion matrix.

FFTrees 1.4

1.4.0

  • Big under the hood changes to make code more efficient (and prepare for C++). Code should be ~50% faster.
  • Many inputs such as cost.cues and cost.outcomes are now specified as named lists to avoid confusion.
  • New cost outputs separate costs from cues, outcomes, and total costs.
  • Changes to input defaults for goal and goal.chase.

FFTrees 1.3

1.3.6

  • Bug fixes.

1.3.5

  • Bug fixes.

1.3.4

  • Added class probability predictions with predict.FFTrees(type = "prob").

  • Updated print.FFTrees() to display FFT #1 ‘in words’ (from the inwords(x) function).

1.3.3

  • Added show.X arguments to plot.FFTrees() that allow you to selectively turn on or turn off elements when plotting an FFTrees object.

  • Added label.tree, label.performance arguments to plot.FFTrees() that allow you to specify plot (sub) labels.

  • Bug fixes:

    • Issues when passing an existing FFTrees object to a new call to FFTrees().

1.3.0

  • Many additional vignettes (e.g.; Accuracy Statistics and Heart Disease Tutorial) and updates to existing vignettes.

  • Added cost.outcomes and cost.cues to allow the user to specify specify the cost of outcomes and cues. Also added a cost statistic throughout outputs.

  • Added inwords(), a function that converts an FFTrees object to words.

  • Added my.tree argument to FFTrees() that allows the user to specify an FFT verbally.
    E.g., my.tree = 'If age > 30, predict True. If sex = {m}, predict False. Otherwise, predict True'.

  • Added positive predictive value ppv, negative predictive value npv and balanced predictive value bpv, as primary accuracy statistics throughout.

  • Added support for two FFT construction algorithms from Martignon et al. (2008): "zigzag" and "max". The algorithms are contained in the file heuristic_algorithm.R and can be implemented in FFTrees() as arguments to algorithm.

FFTrees 1.2

1.2.3

  • Added sens.w argument to allow differential weighting of sensitivities and specificities when selecting and applying trees.

  • Fixed bug in calculating importance weightings from FFForest() outputs.

1.2.0

  • Changed wording of statistics throughout package: hr (hit rate) and far (false alarm rate) (based on the classification frequency values hi and fa), are now sens for sensitivity and spec for specificity (1 −-far), respectively.

  • The rank.method argument is now deprecated. Use algorithm instead.

  • Added a stats argument to plot.FFTrees(). When stats = FALSE, only the tree will be plotted without reference to any statistical output.

  • Grouped all competitive algorithm results (regression, cart, random forests, support vector machines) to the new x.fft$comp slot rather than a separate first level list for each algorithm. Also replaced separate algorithm wrappers with one general comp_pred() wrapper function.

  • Added FFForest(), a function for creating forests of FFTs, and plot.FFForest(), for visualizing forests of FFTs. (This function is experimental and still in development.)

  • Added random forests and support vector machines for comparison in FFTrees() using the randomForest and e1071 packages.

  • Changed logistic regression algorithm from the default glm() version to glmnet() for a regularized version.

  • predict.FFTrees() now returns a vector of predictions for a specific tree rather than creating an entirely new FFTrees object.

  • You can now plot cue accuracies within the plot.FFTrees() function by including the plot.FFTrees(what = 'cues') argument. (This replaces the former showcues() function.)

  • Many cosmetic changes to plot.FFTrees() (e.g.; gray levels, more distinct classification balls). You can also control whether the results from competing algorithms are displayed or not with the comp argument.

  • Bug-fixes:

    • Fixed a bug where levels with no classifications are not plotted correctly.

FFTrees 1.1

1.1.7

  • Trees can now use the same cue multiple times within a tree. To do this, set rank.method = "c" and repeat.cues = TRUE.

  • Bug-fixes:

    • You can (and should!) now have a column of NAs for the criterion in test datasets to represent data where the criterion is unknown.
    • FFTrees() now supports a single predictor (e.g.; formula = diagnosis ~ age) which previously did not work.

1.1.6

  • Streamlined code to improve cohesion between functions. This may cause issues with FFTrees objects created with earlier versions of the package. They will need to be re-created.

  • Updated, clearer print.FFTrees() method to see important info about an FFTrees object in matrix format.

  • Training and testing statistics are now in separate objects (e.g., data$train vs. data$test) to avoid confusion.

  • Bug-fixes:

    • predict.FFTrees() now works much better by passing a new dataset (data.test) as a test dataset for an existing FFTrees object.

1.1.5

  • Bug-fixes:
    • Plotting parameters mar and layout are now reset after running plot.FFTrees()

1.1.4

  • Bug-fixes:
    • Plotting no longer fails when there is only one branch in the tree.
    • Changed which.tree argument in plot.FFTrees() to tree to conform to blog posts.
    • predict.FFTrees() now works better with tibble inputs.
  • Changed the fft label to FFTrees throughout the package to avoid confusion with fast fourier transform. Thus, the main tree building function is now FFTrees() and the new tree object class is FFTrees.

[File NEWS.md last updated on 2025-09-03.]