AutoMLR - Automated Multi-Outcome Machine Learning Combination Models

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>.