yardstick: Tidy Characterizations of Model Performance

Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).

Version: 1.1.0
Depends: R (≥ 3.4.0)
Imports: dplyr (≥ 1.0.9), generics (≥ 0.1.2), hardhat (≥ 1.0.0), rlang (≥ 1.0.2), tidyselect (≥ 1.1.2), utils, vctrs (≥ 0.4.1)
Suggests: covr, crayon, ggplot2, knitr, probably (≥ 0.0.6), rmarkdown, testthat (≥ 3.0.0), tidyr
Published: 2022-09-07
Author: Max Kuhn [aut], Davis Vaughan [aut], Emil Hvitfeldt ORCID iD [aut, cre], RStudio [cph, fnd]
Maintainer: Emil Hvitfeldt <emil.hvitfeldt at rstudio.com>
BugReports: https://github.com/tidymodels/yardstick/issues
License: MIT + file LICENSE
URL: https://github.com/tidymodels/yardstick, https://yardstick.tidymodels.org
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: yardstick results

Documentation:

Reference manual: yardstick.pdf
Vignettes: Metric types
Multiclass averaging

Downloads:

Package source: yardstick_1.1.0.tar.gz
Windows binaries: r-devel: yardstick_1.1.0.zip, r-release: yardstick_1.1.0.zip, r-oldrel: yardstick_1.1.0.zip
macOS binaries: r-release (arm64): yardstick_1.0.0.tgz, r-oldrel (arm64): yardstick_1.0.0.tgz, r-release (x86_64): yardstick_1.1.0.tgz, r-oldrel (x86_64): yardstick_1.1.0.tgz
Old sources: yardstick archive

Reverse dependencies:

Reverse imports: autostats, diceR, forestecology, healthyR.ai, modeltime, modeltime.ensemble, modeltime.resample, probably, shinymodels, stacks, text, tidymodels, treeheatr, trendeval, tune, waywiser
Reverse suggests: baguette, brulee, EZtune, FCPS, finetune, garma, Platypus, sknifedatar, spatialsample, tidyposterior, timetk, vetiver, workflowsets

Linking:

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