Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more information.

Estimate and understand individual-level variation in
    transmission. Implements density and cumulative compound Poisson
    discrete distribution functions (Kremer et al. (2021) 
    <doi:10.1038/s41598-021-93578-x>), as well as functions to calculate
    infectious disease outbreak statistics given epidemiological
    parameters on individual-level transmission; including the probability
    of an outbreak becoming an epidemic/extinct (Kucharski et al. (2020) 
    <doi:10.1016/S1473-3099(20)30144-4>), or the cluster size
    statistics, e.g. what proportion of cases cause X\% of transmission
    (Lloyd-Smith et al. (2005) <doi:10.1038/nature04153>).

Implements a Super Learner framework for right-censored survival data. 
    The package fits convex combinations of parametric, semiparametric, and machine 
    learning survival learners by minimizing cross-validated risk using inverse 
    probability of censoring weighting (IPCW). It provides tools for automated 
    hyperparameter grid search, high-dimensional variable screening, and evaluation 
    of prediction performance using metrics such as the Brier score, Uno's C-index, 
    and time-dependent area under the curve (AUC). Additional utilities support 
    model interpretation for survival ensembles, including Shapley additive 
    explanations (SHAP), and estimation of covariate-adjusted restricted mean 
    survival time (RMST) contrasts. The methodology is related to treatment-specific 
    survival curve estimation using machine learning described by Westling et al.
    (2024) <doi:10.1080/01621459.2023.2205060>, and the unified 
    ensemble framework described in Lyu et al. (2026) <doi:10.64898/2026.03.11.711010>.