R-hardhat
Port variant std
Summary Construct Modeling Packages
Package version 1.4.3
Homepage https://github.com/tidymodels/hardhat
Keywords cran
Maintainer CRAN Automaton
License Not yet specified
Other variants There are no other variants.
Ravenports Buildsheet | History
Ravensource Port Directory | History
Last modified 22 MAY 2026, 14:41:05 UTC
Port created 23 FEB 2022, 02:10:24 UTC
Subpackage Descriptions
single hardhat: Construct Modeling Packages Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input.
Configuration Switches (platform-specific settings discarded)
This port has no build options.
Package Dependencies by Type
Build (only) gmake:primary:std
R:primary:std
icu:dev:std
R:docs:std
Build and Runtime R-cli:single:std
R-glue:single:std
R-rlang:single:std
R-sparsevctrs:single:std
R-tibble:single:std
R-vctrs:single:std
Runtime (only) R:primary:std
R:nls:std
Download groups
main mirror://CRAN/src/contrib
https://loki.dragonflybsd.org/cranfiles/
Distribution File Information
59fb0b932286bca7293dbb47169262a7f53890f6d77ca4aada6b247e979936fe 628032 CRAN/hardhat_1.4.3.tar.gz
Ports that require R-hardhat:std
R-dials:std Tools for Creating Tuning Parameter Values
R-parsnip:std Common API to Modeling and Analysis Functions
R-recipes:std Preprocessing Tools to Create Design Matrices
R-tailor:std Iterative Postprocessing Model Predictions
R-tidymodels:std Easily Install and Load the 'Tidymodels' Packages
R-tune:std Tidy Tuning Tools
R-workflows:std Modeling Workflows
R-workflowsets:std Create a Collection of 'tidymodels' Workflows
R-yardstick:std Tidy Characterizations of Model Performance