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.

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.

Graphical and tabular effect displays, e.g., of interactions, for 
  various statistical models with linear predictors.

Provide utilities to work with indices of effect size for a wide
    variety of models and hypothesis tests (see list of supported models using
    the function 'insight::supported_models()'), allowing computation of and
    conversion between indices such as Cohen's d, r, odds, etc.
    References: Ben-Shachar et al. (2020) <doi:10.21105/joss.02815>.

Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) <doi:10.1016/j.beth.2010.08.006> was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.