Fast, model-agnostic implementation of different H-statistics
    introduced by Jerome H. Friedman and Bogdan E. Popescu (2008)
    <doi:10.1214/07-AOAS148>.  These statistics quantify interaction
    strength per feature, feature pair, and feature triple.  The package
    supports multi-output predictions and can account for case weights.
    In addition, several variants of the original statistics are provided.
    The shape of the interactions can be explored through partial
    dependence plots or individual conditional expectation plots. 'DALEX'
    explainers, meta learners ('mlr3', 'tidymodels', 'caret') and most
    other models work out-of-the-box.

Implements a structured, reproducible framework for budget impact
    modelling (BIM) in health technology assessment (HTA), following the ISPOR
    Task Force guidelines (Sullivan et al. (2014) <doi:10.1016/j.jval.2013.08.2291>
    and Mauskopf et al. (2007) <doi:10.1111/j.1524-4733.2007.00187.x>).
    Provides functions for epidemiology-driven population estimation, market
    share modelling with flexible uptake dynamics, per-patient cost calculation
    across multiple cost categories, multi-year budget projections, payer
    perspective analysis, deterministic sensitivity analysis (DSA), and
    probabilistic sensitivity analysis (PSA) via Monte Carlo simulation.
    Produces submission-quality outputs including ISPOR-aligned summary tables,
    scenario comparison tables, per-patient cost breakdowns, tornado diagrams,
    PSA histograms, and text and HTML reports compatible with NICE, CADTH, and
    EU-HTA dossier formats. Ships with an interactive 'shiny' dashboard built on
    'bslib' for point-and-click model building and exploration.

Implements hierarchical Bayesian inference, robust frequentist
    inference, and distribution-free inference for dependent and unbalanced
    data under strong-mixing conditions. Supports triangular-array,
    weighted-sum and mixingale convergence regimes with Whittle and composite
    likelihoods, heteroskedasticity-and-autocorrelation-consistent variance
    estimation, block bootstrap with automatic block length, fixed-bandwidth
    HAR inference, adaptive conformal prediction, Bayesian decision under
    Region of Practical Equivalence, bridge-sampling Bayes factors, and
    predictive comparison via the Widely Applicable Information Criterion and
    leave-future-out cross-validation. Methods follow Andrews (1991)
    <doi:10.2307/2938229>, Kiefer and Vogelsang (2005)
    <doi:10.1017/S0266466605050565>, Patton, Politis and White (2009)
    <doi:10.1080/07474930802459016>, Vehtari, Gelman and Gabry (2017)
    <doi:10.1007/s11222-016-9696-4>, Kruschke (2018)
    <doi:10.1177/2515245918771304>, and Gibbs and Candes (2021)
    <doi:10.48550/arXiv.2106.00170>.