Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.

Provides 'R6' objects to perform parallelized hyperparameter
    optimization and cross-validation. Hyperparameter optimization can be
    performed with Bayesian optimization (via 'ParBayesianOptimization'
    <https://cran.r-project.org/package=ParBayesianOptimization>) and grid
    search. The optimized hyperparameters can be validated using k-fold
    cross-validation. Alternatively, hyperparameter optimization and
    validation can be performed with nested cross-validation. While
    'mlexperiments' focuses on core wrappers for machine learning
    experiments, additional learner algorithms can be supplemented by
    inheriting from the provided learner base class.

Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.

The Iterative Proportional Fitting (IPF) algorithm operates on count data. 
    This package offers implementations for several algorithms that extend this to 
    nested structures: 'parent' and 'child' items for both of which constraints can be provided.
    The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>,
    Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>,
    and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods
    is also provided such as 'Truncate, replicate, sample' <doi:10.1016/j.compenvurbsys.2013.03.004>.