h2o: R Interface for 'H2O'

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, Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (AutoML).

Depends: R (≥ 2.13.0), methods, stats
Imports: graphics, tools, utils, RCurl, jsonlite
Suggests: ggplot2, mlbench, Matrix, slam, bit64 (≥ 0.9.7), data.table (≥ 1.9.8), survival
Published: 2018-09-25
Author: Erin LeDell [aut, cre], Navdeep Gill [aut], Spencer Aiello [aut], Anqi Fu [aut], Arno Candel [aut], Cliff Click [aut], Tom Kraljevic [aut], Tomas Nykodym [aut], Patrick Aboyoun [aut], Michal Kurka [aut], Michal Malohlava [aut], Ludi Rehak [ctb], Eric Eckstrand [ctb], Brandon Hill [ctb], Sebastian Vidrio [ctb], Surekha Jadhawani [ctb], Amy Wang [ctb], Raymond Peck [ctb], Wendy Wong [ctb], Jan Gorecki [ctb], Matt Dowle [ctb], Yuan Tang [ctb], Lauren DiPerna [ctb], H2O.ai [cph, fnd]
Maintainer: Erin LeDell <erin at h2o.ai>
BugReports: https://0xdata.atlassian.net/projects/PUBDEV
License: Apache License (== 2.0)
URL: https://github.com/h2oai/h2o-3
NeedsCompilation: no
SystemRequirements: Java (>= 7)
Materials: NEWS
In views: HighPerformanceComputing, MachineLearning, ModelDeployment
CRAN checks: h2o results


Reference manual: h2o.pdf
Package source: h2o_3.20.0.8.tar.gz
Windows binaries: r-devel: h2o_3.20.0.8.zip, r-release: h2o_3.20.0.8.zip, r-oldrel: h2o_3.20.0.8.zip
OS X binaries: r-release: h2o_3.20.0.8.tgz, r-oldrel: h2o_3.20.0.8.tgz
Old sources: h2o archive

Reverse dependencies:

Reverse imports: rsparkling
Reverse suggests: exprso, lime, mlr, vip
Reverse enhances: texreg


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