Package: agua 0.1.4.9000

Qiushi Yan

agua: 'tidymodels' Integration with 'h2o'

Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The package enables users to specify 'h2o' as an engine for several modeling methods.

Authors:Max Kuhn [aut], Qiushi Yan [aut, cre], Steven Pawley [aut], Posit Software, PBC [cph, fnd]

agua_0.1.4.9000.tar.gz
agua_0.1.4.9000.zip(r-4.7)agua_0.1.4.9000.zip(r-4.6)agua_0.1.4.9000.zip(r-4.5)
agua_0.1.4.9000.tgz(r-4.6-any)agua_0.1.4.9000.tgz(r-4.5-any)
agua_0.1.4.9000.tar.gz(r-4.7-any)agua_0.1.4.9000.tar.gz(r-4.6-any)
agua_0.1.4.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
agua/json (API)

# Install 'agua' in R:

install.packages('agua', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/agua/issues

Pkgdown/docs site:https://agua.tidymodels.org

On CRAN:

6.79 score 25 stars 122 scripts 929 downloads 35 exports 105 dependencies

Last updated from:dfc7a13ad3. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	213
source / vignettes	OK	222
linux-release-x86_64	OK	226
macos-release-arm64	OK	204
macos-oldrel-arm64	OK	112
windows-devel	OK	172
windows-release	OK	166
windows-oldrel	OK	147
wasm-release	OK	155

Exports:%>%agua_backend_options as_h2o as_tibble autoplot collect_metrics extract_fit_engine extract_fit_parsnip get_leaderboard h2o_activation h2o_end h2o_get_frame h2o_get_model h2o_predict h2o_predict_classification h2o_predict_regression h2o_remove h2o_remove_all h2o_running h2o_split h2o_start h2o_train h2o_train_auto h2o_train_gbm h2o_train_glm h2o_train_mlp h2o_train_nb h2o_train_rf h2o_train_rule h2o_train_xgboost h2o_xgboost_available member_weights rank_results refit tidy

Dependencies:base64enc bitops bslib cachem class cli clock codetools cpp11 data.table diagram dials DiceDesign digest dplyr evaluate farver fastmap fontawesome fs furrr future future.apply GauPro generics ggplot2 globals glue gower gtable h2o hardhat highr htmltools ipred isoband jquerylib jsonlite KernSmooth knitr labeling lattice lava lbfgs lifecycle listenv lubridate magrittr MASS Matrix memoise mime mixopt modelenv nnet numDeriv parallelly parsnip pillar pkgconfig prettyunits prodlim progressr purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo RCurl recipes rlang rmarkdown rpart rsample S7 sass scales sfd shape slider sparsevctrs splitfngr SQUAREM stringi stringr survival tailor tibble tidyr tidyselect timechange timeDate tinytex tune tzdb utf8 vctrs viridisLite warp withr workflows xfun yaml yardstick

Introduction to agua

Introduction | Fitting models with the 'h2o' engine

Rendered fromagua.Rmdusingknitr::rmarkdown

Last update: 2022-10-12
Started: 2022-07-12

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Control model tuning via 'h2o::h2o.grid()'	agua_backend_options
Data conversion tools	as_h2o as_tibble.H2OFrame
Plot rankings and metrics of H2O AutoML results	autoplot.H2OAutoML autoplot.workflow
Prediction wrappers for h2o	h2o_predict h2o_predict_classification h2o_predict_regression predict._H2OAutoML
Utility functions for interacting with the h2o server	h2o_end h2o_get_frame h2o_get_model h2o_remove h2o_remove_all h2o_running h2o_start h2o_xgboost_available
Model wrappers for h2o	h2o_train h2o_train_auto h2o_train_gbm h2o_train_glm h2o_train_mlp h2o_train_nb h2o_train_rf h2o_train_rule h2o_train_xgboost
Tools for working with H2O AutoML results	collect_metrics.H2OAutoML collect_metrics.workflow collect_metrics._H2OAutoML extract_fit_engine._H2OAutoML extract_fit_parsnip._H2OAutoML get_leaderboard member_weights rank_results.H2OAutoML rank_results.workflow rank_results._H2OAutoML refit.workflow refit._H2OAutoML tidy._H2OAutoML