Provides automated machine learning workflows for survival
    analysis, binary classification, continuous outcomes, and ordinal outcomes.
    The package trains and combines model variants across user-supplied
    multi-cohort data, evaluates survival models by leave-one-out
    cross-validation using Harrell's concordance index, binary models by
    leave-one-out cross-validation using receiver operating characteristic
    area under the curve, continuous models by out-of-fold root mean squared
    error and R-squared, and ordinal models by out-of-fold quadratic weighted
    kappa. It
    renders reproducible reports in Hypertext Markup Language (HTML) with
    figures and diagnostics. The survival workflow supports penalized and
    tree-based Cox proportional hazards models, stepwise Cox models, partial
    least squares regression for Cox models, supervised principal components,
    gradient boosting machine Cox models, survival support vector
    machines (survival-SVM), random survival forests, and optional 'CoxBoost'.
    The binary workflow supports penalized logistic regression, logistic
    baselines, gradient boosting machines, random forests, principal component
    analysis (PCA) logistic regression, and Gaussian naive Bayes variants.
    Continuous and ordinal workflows reuse an 18-variant regression registry
    with penalized, linear, boosted, forest, PCA, and baseline families. The
    optional 'CoxBoost' model is enabled when the suggested 'CoxBoost' package
    is installed; it is used conditionally and is not a strong dependency.
    Optional model backends are checked at run time so missing backend packages
    skip only the affected model variants rather than blocking installation of
    the whole package.
    Methods build on Friedman et al. (2010) <doi:10.18637/jss.v033.i01>,
    Bair and Tibshirani (2004) <doi:10.1371/journal.pbio.0020108>, Ishwaran
    et al. (2008) <doi:10.1214/08-AOAS169>, Blanche et al. (2013)
    <doi:10.1002/sim.5958>, and Binder and Schumacher (2008)
    <doi:10.1186/1471-2105-9-14>.