R interface for 'H2O', the scalable open source machine learning
    platform that offers parallelized implementations of many supervised and
    unsupervised machine learning algorithms such as Generalized Linear
    Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests,
    Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes,
    Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection,
    Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Interface to 'H2O4GPU' <https://github.com/h2oai/h2o4gpu>, a collection of 'GPU' solvers for machine learning algorithms.

Several functions are provided that simplify using 'h2o' 
             package. Currently, a function for extracting the AutoML 
             model parameter is provided, alongside a function for computing 
             F-Measure statistics at any given threshold. For more information 
             about 'h2o' package see <https://h2o.ai/>.

Implements the sample size methods for hierarchical 2x2 factorial trials under two choices of effect estimands and a series of hypothesis tests proposed in "Sample size calculation in hierarchical 2x2 factorial trials with unequal cluster sizes" (under review), and provides the table and plot generators for the sample size estimations.

Provides access to Uber's H3 library for geospatial indexing via its JavaScript transpile 'h3-js' <https://github.com/uber/h3-js> and 'V8' <https://github.com/jeroen/v8>.

'H3' is a hexagonal hierarchical spatial index developed by 'Uber' <https://h3geo.org/>.
  This package exposes the source code of 'H3' (written in 'C') to routines that are callable through 'R'.

Provides access to Uber's 'H3' geospatial indexing system via 'h3lib' 
  <https://CRAN.R-project.org/package=h3lib>. 'h3r' is designed to mimic the 'H3' 
  Application Programming Interface (API) <https://h3geo.org/docs/api/indexing/>, 
  so that any function in the API is also available in 'h3r'.