Implements the Information Matrix test for regression models following Cameron, A. C., & Trivedi, P. K. (1990) <https://cameron.econ.ucdavis.edu/research/imtest_impliedalternatives_ucdwp372.pdf> Decomposes the test into components for heteroscedasticity, skewness, and kurtosis to diagnose specific forms of misspecification. Provides both overall and component-wise statistics for model assessment.

Implements various measures of information theory based on several entropy estimators.

Implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) <doi:10.1103/PhysRevE.69.066138>, knockoff conditional mutual information described in Zhang & Chen (2025) <doi:10.1126/sciadv.adu6464>, synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>, allowing detection of complex and diverse relationships among variables.

Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated, as described in Rousset et al. (2026) <doi:10.24072/pcjournal.721>.

Collection of tools for assessment of feature importance and feature effects.
    Key functions are:
    feature_importance() for assessment of global level feature importance,
    ceteris_paribus() for calculation of the what-if plots,
    partial_dependence() for partial dependence plots,
    conditional_dependence() for conditional dependence plots,
    accumulated_dependence() for accumulated local effects plots,
    aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,
    generic print() and plot() for better usability of selected explainers,
    generic plotD3() for interactive, D3 based explanations, and
    generic describe() for explanations in natural language.
    The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.