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orbital: Predict with 'tidymodels' Workflows in Databases

Turn 'tidymodels' workflows into objects containing the sufficient sequential equations to perform predictions. These smaller objects allow for low dependency prediction locally or directly in databases.

Version: 0.3.0
Imports: cli, dplyr, generics (≥ 0.1.2), rlang
Suggests: arrow, bonsai, DBI, dbplyr, dtplyr, duckdb, embed, glue, gt, hardhat, jsonlite, kknn, knitr, modeldata, parsnip, partykit, R6, recipes, rmarkdown, RSQLite, rstanarm, sparklyr, testthat (≥ 3.0.0), themis, tibble, tidypredict (≥ 0.5.1), workflows
Published: 2024-12-22
Author: Emil Hvitfeldt [aut, cre], Posit Software, PBC [cph, fnd]
Maintainer: Emil Hvitfeldt <emil.hvitfeldt at posit.co>
BugReports: https://github.com/tidymodels/orbital/issues
License: MIT + file LICENSE
URL: https://github.com/tidymodels/orbital, https://orbital.tidymodels.org
NeedsCompilation: no
Materials: README NEWS
CRAN checks: orbital results

Documentation:

Reference manual: orbital.pdf
Vignettes: Supported Models and recipes steps (source, R code)

Downloads:

Package source: orbital_0.3.0.tar.gz
Windows binaries: r-devel: orbital_0.2.0.zip, r-release: orbital_0.2.0.zip, r-oldrel: orbital_0.2.0.zip
macOS binaries: r-release (arm64): orbital_0.2.0.tgz, r-oldrel (arm64): orbital_0.2.0.tgz, r-release (x86_64): orbital_0.2.0.tgz, r-oldrel (x86_64): orbital_0.2.0.tgz
Old sources: orbital archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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