Robust covariance estimation for matrix-valued data and data with Kronecker-covariance structure using the Matrix Minimum Covariance Determinant (MMCD) estimators and outlier explanation using and Shapley values.

Provides tools for causal mediation analysis with continuous
    treatments using inverse probability weighting (IPW). Estimates natural
    direct and indirect effects over a user-defined treatment grid and supports
    flexible dose-response mediation analysis. Includes diagnostic procedures
    for assessing covariate balance in both treatment and mediator models using
    standardized mean differences. Implements pathway-specific extensions of
    the impact threshold for a confounding variable (ITCV; Frank, 2000
    <doi:10.1177/0049124100029002001>) adapted to mediation settings. Provides
    joint sensitivity analysis combining E-values (VanderWeele and Ding, 2017
    <doi:10.7326/M16-2607>) and violations of sequential ignorability (Imai,
    Keele, and Yamamoto, 2010 <doi:10.1214/10-STS321>). Additional utilities
    include visualization of dose-response mediation functions, robustness
    profiles, fragility summaries, and formatted outputs for applied research.
    Supports clustered data structures and multiple outcome families.

Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) <doi:10.1093/biomet/85.3.549>, and the meta-analysis methods are developed by Noma et al. (2022) <forthcoming>.

Calculates robust Matthews Correlation Coefficient (MCC) and robust F-Beta Scores, as introduced by Holzmann and Klar (2024) <doi:10.48550/arXiv.2404.07661>. 
    These performance metrics are designed for imbalanced classification problems.
    Plots the receiver operating characteristic curve (ROC curve) together with the recall / 1-precision curve.