summary() methods have been implemented for ALE and ModelBoot objects. They print a summary of ALE statistics to the console. When there are no ALE statistics available, they print a message saying so.aled_fun argument of the ALE() and ALEpDist() constructors, we may optionally choose to calculate ALE deviation based on standard deviations instead of the default mean absolute deviation.min_col_widths argument of plot.ALE().consolid_cats argument of plot.ALE()). The top consolid_cats - 1 values in ALE strength are shown and all other values are consolidated into an "other" category."levels" of the fct_order argument to the ALE() constructor, unordered factors are now sorted in the order of the factor levels (characters columns are sorted in alphabetical order of their unique values). An alternative "y_col" sorts based on the increasing mean values of the predictions of the outcome variable for each factor level. The "ksd" option allows compatibility with the original ALEPlot reference implementation.max_num_bins argument to the ALE() constructor now accepts a special list format that allows the specification of per-column maximum ALE bin sizes for numeric input columns, with a default for columns not named. For details, see documentation for ALE().ale.parallel. For example, you can set 4 CPU cores with options(ale.parallel = 4).{ranger} package is automatically recognized so that y_col and pred_fun don't need to be specified. In the future, a very few other very popular packages and frameworks will also be automatically recognized, but there will be no attempt to cover most packages: pred_fun remains the general way to customize predict functions for packages that don't work out of the box.We have dealt with innumerable bugs during our development journey but, fortunately, very few publicly signalled bugs. Only fixes for publicly reported bugs are indicated here.
ALE() now warns early about requested predictors with only one observed value and returns zero main and interaction effects for them, including bootstrap iterations.nnet::nnet(), are now accepted and named after the outcome column.get() for ALE and ModelBoot objects now returns named statistics in the requested order within each term.get() now raises a clear error when confidence regions are requested without p-values. summary() for ALE and ModelBoot objects silently omits confidence-region sections when p-values are absent.customize() calls with zoom_x or zoom_y now preserve the existing limits of the other axis.ALE() now checks the outcome column and prediction type when accepting an ALEpDist distribution, in addition to the model identity. ALEpDist retains the original prediction settings, including for surrogates.parallelly::availableCores() directly in preparation for its re-export being removed from {future}. The {future} and {parallelly} packages are now suggested dependencies because both are required dependencies of the imported {furrr} package (#18).require_same_p argument of ALE() has been renamed to require_p_model_match without a compatibility alias. Setting it to FALSE disables all p-value compatibility checks.max_num_bins now creates n+1 bins instead of exactly the specified n bins. The bottom bin is reserved for the minimum value in the data. This is a reversion to the original behaviour of the reference {ALEPlot} package.parallel = 0) (#16).pred_fun argument in ALE() and other constructors now defaults to NULL. However, the functionality is unchanged: the default still creates the same generic custom prediction function as before.We now use OpenAI Codex to help development and maintenance. Codex changes are not specifically highlighted in this change log, but numerous PRs are heavily assisted by Codex.
ALEPlots by appending ggplot layers with the customize() function.invert_probs() inverts probabilities (subtracts from 1) for ALE and ALEpDist objects.We have deeply rethought the vision of this package and have completely rewritten the entire package to support existing, new, and future planned functionality. The changes are so radical that there is no continuity with the previous version 0.3.1. Thus, we've skipped a version number and now are at version 0.5.0.
Honestly, we can't keep track of all the changes; experienced users are advised to rerun the vignettes to get up to speed with the new version. We apologize for the discontinuity but we trust that the latest version is easier to use and much more functional. What follows is a list of some of the most notable changes we've kept track of.
ale package objects has been completely rewritten. The latest objects are not compatible with earlier versions. However, the new structure supports the roadmap of future functionality, so we hope that there will be minimal changes in the future that interrupt backward compatibility.{S7} classes to represent different kinds of ale package objects:
ALE: the core ale package object that holds ALE data for a model (replaces the former ale() and ale_ixn() functions).ModelBoot: results of full-model bootstrapping (replaces the former model_bootstrap() function).ALEPlots: store ALE plots generated from either ALE or ModelBoot with convenient print() and plot() methods.ALEpDist: p-value distribution information (replaces the former create_p_dist() function).{ALEPlot} package code and so now claim full authorship of the code. One of the most significant implications of this is that we have decided to change the package license from the GPL 2 to MIT, which permits maximum dissemination of our algorithms.ale_ixn() has been eliminated and now both 1D and 2D ALE are calculated with the ALE() constructor.ALE object constructor no longer produces plots directly. ALE plots are now created as ALEPlots objects using the newly added plot() methods that create all possible plots from the ALE data from ALE or ModelBoot objects. Thus, serializing ALE objects now avoids the previous problems of environment bloat of the included ggplot objects.rug_sample_size argument of the ALE constructor to sample_size. Now it reflects the size of data that should be sampled in the ale object, which can be used not only for rug plots but for other purposes.x_cols argument in ALE() now supports a complex syntax for specifying which specific columns for 1D ALE or pairs of columns for 2D interactions are desired. It also supports specification using standard R formula syntax.get() methods now provide convenient access to ALE, ModelBoot, and ALEPlots objects.plot() methods, eliminated the compact_plots to ale().print() and plot() methods have been added to the ale_plots object.print() method has been added to the ALE object.model_bootstrap() has added various model performance measures that are validated using bootstrap validation with the .632 correction.p_funs has been completely changed; it has now been converted to an object named ale_p and the functions are separated from the object as internal functions. The function create_p_funs() has been renamed create_p_dist().ALEpDist() now produces three types of p-values: "exact" (very slow) with at least 1000 random iterations on the original model; "approx" for 100 to 999 iterations on the original model; and "surrogate" for much faster but less reliable p-values based on a surrogate linear model. See ALEpDist() for details.We have dealt with innumerable bugs during our development journey but, fortunately, very few publicly signalled bugs. Only fixes for publicly reported bugs are indicated here.
One of the most fundamental changes is not directly visible but affects how some ALE values are calculated. In certain very specific cases, the ALE values are now slightly different from those of the reference {ALEPlot} package. These are only for non-numerical variables for some prediction types other than predictions scaled on the response variable. (E.g., a binary or categorical variable for a logarithmic prediction not scaled to the same scale as the response variable.) We made this change for two reasons:
{ALEPlot} implementation. These cases are not covered at all in the base ALE scientific article and they are poorly documented in the {ALEPlot} code. We cannot help users to interpret results that we do not understand ourselves.{ALEPlot} reference implementation is not scalable: custom code must be written for each type and each degree of interaction.Other than for these edge cases, our implementation continues to give identical results to the reference {ALEPlot} package.
Other notable changes that might not be readily visible to users:
{staccuracy}.{rlang} and {cli} packages. Reduced the imported functions to a minimum.{cli}.{assertthat} with custom validation functions that adapt some {assertthat} code.helper.R test files so that some testing objects are available to the loaded package.{future} parallelization code to restore original values on exit.ale_p objects.{ggplot2} 3.5.{covr}.The most significant updates are the addition of p-values for the ALE statistics, the launching of a pkgdown website which will henceforth host the development version of the package, and parallelization of core functions with a resulting performance boost.
One of the key goals for the ale package is that it would be truly model-agnostic: it should support any R object that can be considered a model, where a model is defined as an object that makes a prediction for each input row of data that it is provided. Towards this goal, we had to adjust the custom predict function to make it more flexible for various kinds of model objects. We are happy that our changes now enable support for tidymodels objects and various survival models (but for now, only those that return single-vector predictions). So, in addition to taking required object and newdata arguments, the custom predict function pred_fun in the ale() function now also requires an argument for type to specify the prediction type, whether it is used or not. This change breaks previous code that used custom predict functions, but it allows ale to analyze many new model types than before. Code that did not require custom predict functions should not be affected by this change. See the updated documentation of the ale() function for details.
Another change that breaks former code is that the arguments for model_bootstrap() have been modified. Instead of a cumbersome model_call_string, model_bootstrap() now uses the {insight} package to automatically detect many R models and directly manipulate the model object as needed. So, the second argument is now the model object. However, for non-standard models that {insight} cannot automatically parse, a modified model_call_string is still available to assure model-agnostic functionality. Although this change breaks former code that ran model_bootstrap(), we believe that the new function interface is much more user-friendly.
A slight change that might break some existing code is that the conf_regions output associated with ALE statistics has been restructured. The new structure provides more useful information. See help(ale) for details.
pkgdown website located at https://tripartio.github.io/ale/. This is where the most recent development features will be documented.create_p_funs() function for details and an example.vignette('ale-statistics') for details. The vignette has been expanded with more details on how to properly interpret normalized ALE statistics.vignette('ale-statistics') for details.{furrr} library. In our tests, practically, we typically found speed-ups of n – 2 where n is the number of physical cores (machine learning is generally unable to use logical cores). For example, a computer with 4 physical cores should see at least ×2 speed-up and a computer with 6 physical cores should see at least ×4 speed-up. However, parallelization is tricky with our model-agnostic design. When users work with models that follow standard R conventions, the ale package should be able to automatically configure the system for parallelization. But for some non-standard models users may have to explicitly list the model's packages in the new model_packages argument so that each parallel thread can find all necessary functions. This is only a concern if you get weird errors. See help(ale) for details.ale() function. See help(ale) for details.median_band_pct argument to ale() now takes a vector of two numbers, one for the inner band and one for the outer.{gridExtra} with {patchwork} for examples and vignettes for printing plots.ale() function documentation from ale-package documentation.alt tags to describe plots for accessibility.{insight} package to automatically detect y_col and model call objects when possible; this increases the range of automatic model detection of the ale package in general.{progressr} package for progress bars. With the cli progression handler, this enables accurate estimated times of arrival (ETA) for long procedures, even with parallel computing. A message is displayed once per session informing users of how to customize their progress bars. For details, see help(ale), particularly the documentation on progress bars and the silent argument.{ggplot2} from a dependency to an import. So, it is no longer automatically loaded with the package.var_summary() function. In particular, encodes whether the user is using p-values (ALER band) or not (median band).validation.R file.compact_plots to plotting functions to strip plot environments to reduce the size of returned objects. See help(ale) for details.package_scope environment.ale_ixn()).ale_ixn()).ale() does not yet support multi-output model prediction types (e.g., multi-class classification and multi-time survival probabilities).This version introduces various ALE-based statistics that let ALE be used for statistical inference, not just interpretable machine learning. A dedicated vignette introduces this functionality (see "ALE-based statistics for statistical inference and effect sizes" from the vignettes link on the main CRAN page at https://CRAN.R-project.org/package=ale). We introduce these statistics in detail in a working paper: Okoli, Chitu. 2023. "Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE)." arXiv. https://doi.org/10.48550/arXiv.2310.09877. Please note that they might be further refined after peer review.
ale() and model_bootstrap() now output these statistics. (ale_ixn() will come later.)ale package with the reference {ALEPlot} package: "Comparison between {ALEPlot} and ale packages" (available from the vignettes link on the main CRAN page at https://CRAN.R-project.org/package=ale).var_cars is a modified version of mtcars that features many different types of variables.census is a polished version of the adult income dataset used for a vignette in the {ALEPlot} package.silent = TRUE to ale(), ale_ixn(), or model_bootstrap().seed argument to ale(), ale_ixn(), or model_bootstrap().By far the most extensive changes have been to assure the accuracy and stability of the package from a software engineering perspective. Even though these are not visible to users, they make the package more robust with hopefully fewer bugs. Indeed, the extensive data validation may help users debug their own errors.
{assertthat} package; if not, the function fails quickly with an appropriate error message.{testthat} package is now used for testing the outputs of each user-facing function. This should help the code base to be more robust going forward with future developments.{ALEPlot} package. These tests should ensure that any future code that breaks the accuracy of ALE calculations will be caught quickly.ale_ixn()).ale_ixn()).This is the first CRAN release of the ale package. Here is its official description with the initial release:
Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. (Apley, Daniel W., and Jingyu Zhu. "Visualizing the effects of predictor variables in black box supervised learning models." Journal of the Royal Statistical Society Series B: Statistical Methodology 82.4 (2020): 1059-1086 doi:10.1111/rssb.12377.) ALE has two primary advantages over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values are not affected by the presence of interactions among variables in a model and its computation is relatively rapid. This package rewrites the original code from the 'ALEPlot' package for calculating ALE data and it completely reimplements the plotting of ALE values.
(This package uses the same GPL-2 license as the {ALEPlot} package.)
This initial release replicates the full functionality of the {ALEPlot} package and a lot more. It currently presents three functions:
ale(): create data for and plot one-way ALE (single variables). ALE values may be bootstrapped.ale_ixn(): create data for and plot two-way ALE interactions. Bootstrapping of the interaction ALE values has not yet been implemented.model_bootstrap(): bootstrap an entire model, not just the ALE values. This function returns the bootstrapped model statistics and coefficients as well as the bootstrapped ALE values. This is the appropriate approach for small samples.This release provides more details in the following vignettes (they are all available from the vignettes link on the main CRAN page at https://CRAN.R-project.org/package=ale):
ale packageale() function handling of various datatypes for xThe version numbering system for the {ale} package is an adaptation of the standard three-number system. So, in a version number of M.m.b (e.g., 0.5.3), the numbers mean:
That said, major version 0 has special meaning: it indicates that the package is in beta, that is, still in experimental status, meaning that a stable interface has not yet been settled. New features might be written that break the functionality of previous 0.x versions. That said, every minor version is tested with {revdepcheck}. A notification or pull request will be sent to the maintainers of any packages whose functionality might be broken due to a change in {ale} functionality.
Normally, if new functionality is added along with bug fixes, then the bug fixes will be released with the next minor version. However, with the major version 0, small changes in functionality will sometimes be released in a bug-fix version.
The numbering system described above is used for packages released to CRAN. For development versions in between CRAN releases, the {ale} package adds a fourth component D, that is, an M.m.b.D format. The D is in YYYYMMDD date format, indicating the release date of the latest development version after the latest CRAN release. So, version 0.5.3.20260217 is a development version released on February 17, 2026, after the 0.5.3 CRAN version. However, as the specific date keeps on changing, the latest development version is simply indicated in the change log below as "ale (development version)".