Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) <doi:10.1162/REST_a_00639>.  The effective number of clusters is a statistic to assess the reliability of asymptotic inference when sampling or treatment assignment is clustered. Methods are implemented for stats::lm(), plm::plm(), and fixest::feols(). There is also a formula method.

A conservative, assumption-aware statistical consistency checker for
    published research results. Parses test statistics, effect sizes, and confidence
    intervals from text, PDF, HTML, and Word documents across multiple citation styles
    including American Psychological Association (APA), Harvard, Frontiers, PLOS ONE,
    Scientific Reports, Nature Human Behaviour, PeerJ, eLife, PNAS, and others.
    Recomputes effect sizes using all plausible variants when design is ambiguous, and
    validates internal consistency. Supports t-tests, F-tests/ANOVA, correlations,
    chi-square, z-tests, regression, and nonparametric tests. Provides
    'statcheck'-compatible API functions for batch processing of files and directories.
    Explicitly tracks all assumptions and uncertainty in output. Detects decision errors
    (significance reversals) similar to 'statcheck'. Note: this package is under active
    development and results should be independently verified. Use is at the sole
    responsibility of the user. Contributions and verification reports are welcome.

Use structural equation modeling to estimate average and
    conditional effects of a treatment variable on an outcome variable, taking into
    account multiple continuous and categorical covariates.

High-performance implementation of various effect plots
    useful for regression and probabilistic classification tasks.  The
    package includes partial dependence plots (Friedman, 2021,
    <doi:10.1214/aos/1013203451>), accumulated local effect plots and
    M-plots (both from Apley and Zhu, 2016, <doi:10.1111/rssb.12377>), as
    well as plots that describe the statistical associations between model
    response and features.  It supports visualizations with either
    'ggplot2' or 'plotly', and is compatible with most models, including
    'Tidymodels', models wrapped in 'DALEX' explainers, or models with
    case weights.