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'.

A Haar-Fisz algorithm for Poisson intensity estimation.
	Will denoise Poisson distributed sequences where
        underlying intensity is not constant. Uses the multiscale
        variance-stabilization method called the Haar-Fisz transform.
        Contains functions to carry out the forward and inverse
        Haar-Fisz transform and denoising on near-Gaussian sequences.
        Can also carry out cycle-spinning.
	Main reference: Fryzlewicz, P. and Nason, G.P. (2004)
	"A Haar-Fisz algorithm for Poisson intensity estimation."
        Journal of Computational and Graphical Statistics,
        13, 621-638. <doi:10.1198/106186004X2697>.