stats, utils, dplyr, tidyr (>= 1.0.0), rlang, rpart,
rpart.plot, ggplot2, ggpubr, MetricsWeighted (>= 0.3.0)

stats, utils, tidyselect, dplyr (>= 1.0.0), tidyr (>= 1.0.0),
rpart, rpart.plot, ggplot2, ggpubr, MetricsWeighted (>= 0.3.0)
knitr, rmarkdown, testthat, ranger, xgboost, moderndive,
caret, mlr3, mlr3learners

stats, utils, tidyselect, dplyr (>= 1.0.0), tidyr (>= 1.0.0),
rpart, rpart.plot, ggplot2, cowplot, MetricsWeighted (>= 0.3.0)

Shed light on black box machine learning models by the help
of model performance, variable importance, global surrogate models,
ICE profiles, partial dependence (Friedman J. H. (2001)
<doi:10.1214/aos/1013203451>), accumulated local effects (Apley D. W.
(2016) <arXiv:1612.08468>), further effects plots, scatter plots,
interaction strength, and variable contribution breakdown (approximate
SHAP) for single observations (Gosiewska and Biecek (2019)
<arxiv:1903.11420>). All tools are implemented to work with case
weights and allow for stratified analysis. Furthermore, multiple
flashlights can be combined and analyzed together.
dplyr (>= 1.0.0), cowplot, ggplot2, MetricsWeighted (>=
0.3.0), rpart, rpart.plot, stats, tidyr (>= 1.0.0), tidyselect,
utils, withr
knitr, caret, mlr3, mlr3learners, moderndive, ranger,
rmarkdown, testthat, xgboost

Create Flashcards of Terms and Definitions
person("Jeffrey R.", "Stevens", , "jeffrey.r.stevens@protonmail.com",
role = c("aut", "cre", "cph"),
comment = c(ORCID = "0000-0003-2375-1360"))
Provides functions for creating flashcard decks of terms and
definitions. This package creates HTML slides using 'revealjs' that can be
viewed in the 'RStudio' viewer or a web browser.  Users can create
flashcards from either existing built-in decks or create their own from CSV
files or vectors of function names.
cli, curl, gh, httr, memoise, revealjs, rmarkdown, testthat
Jeffrey R. Stevens [aut, cre, cph]
(<https://orcid.org/0000-0003-2375-1360>)

Indices and Tests for Assessing the Flatness of (Rank)
Histograms
person("Michaël", "Zamo", email = "michael.zamo@meteo.fr", role = c("aut", "cre"))
S3 classes, plotting functions, indices and tests to analyse the flatness of histograms.
It is specifically (but not only) aimed for assessing whether "rank" histograms (much used in weather forecasting) are flat.
Specifically functions are provided to use the Jolliffe-Primo flatness tests introduced in Jolliffe and Primo (2008, <doi:10.1175/2007MWR2219.1>).
Flatness indices described in Wilks (2019, <doi:10.1175/MWR-D-18-0369.1>) can be computed.
Finally a function to use the Benjamini-Hochberg procedure for multiple hypothesis testing is provided (Benjamini and Hochberg, 1995, <doi:10.1111/j.2517-6161.1995.tb02031.x>).