Enhances 'mlexperiments'
    <https://CRAN.R-project.org/package=mlexperiments> with additional
    machine learning ('ML') learners. The package provides R6-based
    learners for the following algorithms: 'glmnet'
    <https://CRAN.R-project.org/package=glmnet>, 'ranger'
    <https://CRAN.R-project.org/package=ranger>, 'xgboost'
    <https://CRAN.R-project.org/package=xgboost>, and 'lightgbm'
    <https://CRAN.R-project.org/package=lightgbm>. These can be used
    directly with the 'mlexperiments' R package.

Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.

An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008)
    <doi:10.1287/opre.1070.0496>, Heinrich (1998) <doi:10.1006/jcom.1998.0471>,
    for R.  This package builds on the original 'Matlab' and 'C++' implementations
    by Mike Giles to provide a full MLMC driver and example level samplers.
    Multi-core parallel sampling of levels is provided built-in.

A collection of evaluation metrics, including loss, score and
    utility functions, that measure regression, classification and ranking performance.

A collection of miscellaneous helper function for running multilevel/mixed models in 'lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015).

Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.