Suggests: ggplot2, mlbench, Matrix, slam, bit64 (>= 0.9.7), data.table
Collate: 'astfun.R' 'automl.R' 'classes.R' 'config.R' 'connection.R'
'kvstore.R' 'frame.R' 'import.R' 'parse.R' 'export.R'
'edicts.R' 'models.R' 'kmeans.R' 'gbm.R' 'glm.R' 'glrm.R'
'word2vec.R' 'w2vutils.R' 'locate.R' 'grid.R' 'predict.R'

Collate: 'aggregator.R' 'astfun.R' 'automl.R' 'classes.R' 'config.R'
'communication.R' 'kvstore.R' 'frame.R' 'import.R' 'parse.R'
'export.R' 'edicts.R' 'models.R' 'kmeans.R' 'gbm.R' 'glm.R'
'glrm.R' 'pca.R' 'svd.R' 'deeplearning.R' 'stackedensemble.R'

person(given = "Tom", family = "Kraljevic", role = c("aut", "cre"), email = "tomk@0xdata.com"),
person(given = "H2O.ai", role = c("cph"))
Description: R scripting functionality for 'H2O', the open source
'export.R' 'edicts.R' 'models.R' 'coxph.R' 'coxphutils.R'
'kmeans.R' 'gbm.R' 'glm.R' 'glrm.R' 'pca.R' 'svd.R'

person("Erin", "LeDell", role = c("aut", "cre"), email = "erin@h2o.ai"),
person("Navdeep", "Gill", role = c("aut")),
person("Spencer", "Aiello", role = c("aut")),
person("Arno", "Candel", role = c("aut")),
person("Cliff", "Click", role = c("aut")),
person("Tom", "Kraljevic", role = c("aut")),
person("Tomas", "Nykodym", role = c("aut")),
person("Patrick", "Aboyoun", role = c("aut")),
person("Michal", "Kurka", role = c("aut")),
person("Michal", "Malohlava", role = c("aut")),
person("Ludi", "Rehak", role = c("ctb")),
person("Eric", "Eckstrand", role = c("ctb")),
person("Brandon", "Hill", role = c("ctb")),
person("Sebastian", "Vidrio", role = c("ctb")),
person("Surekha", "Jadhawani", role = c("ctb")),
person("Raymond", "Peck", role = c("ctb")),
person("Wendy", "Wong", role = c("ctb")),
person("Jan", "Gorecki", role = c("ctb")),
person("Matt", "Dowle", role = c("ctb")),
person("Yuan", "Tang", role = c("ctb")),
person("Lauren", "DiPerna", role = c("ctb")),
person("H2O.ai", role = c("cph", "fnd"))
Description: 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