c(person(given = "Indrajeet",
family = "Patil",
role = c("cre", "aut", "cph"),
email = "patilindrajeet.science@gmail.com",
comment = c(ORCID = "0000-0003-1995-6531", Twitter = "@patilindrajeets")),
person(given = "Chuck",
family = "Powell",
email = "ibecav@gmail.com",
role = "ctb",
comment = c(ORCID = "0000-0002-3606-2188")))
Extension of 'ggplot2', 'ggstatsplot' creates graphics with
details from statistical tests included in the plots themselves. It
provides an easier syntax to generate information-rich plots for
statistical analysis of continuous (violin plots, scatterplots,
histograms, dot plots, dot-and-whisker plots) or categorical (pie and
bar charts) data. Currently, it supports the most common types of
statistical approaches and tests: parametric, nonparametric, robust,
and Bayesian versions of t-test/ANOVA, correlation analyses,
contingency table analysis, meta-analysis, and regression analyses.
References: Patil (2021) <doi:10.21105/joss.03236>.
correlation (>= 0.8.3), datawizard (>= 0.6.4), dplyr, ggplot2
(>= 3.4.0), ggrepel, ggsignif, glue, insight (>= 0.18.8),
paletteer, parameters (>= 0.20.0), patchwork, performance (>=
0.10.1), purrr, rlang, stats, statsExpressions (>= 1.3.5),
tidyr, utils
afex, BayesFactor (>= 0.9.12-4.4), gapminder, ggcorrplot (>=
0.1.4), ggside (>= 0.2.1), knitr, lme4, MASS, metaBMA, metafor,
metaplus, PMCMRplus, psych, rmarkdown, spelling, survival,
testthat (>= 3.1.5), tibble, vdiffr, WRS2
Indrajeet Patil [cre, aut, cph]
(<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets),
Chuck Powell [ctb] (<https://orcid.org/0000-0002-3606-2188>)

broom, broom.mixed, knitr, lme4, MASS, MCMCglmm, mixor,
ordinal, rmarkdown, spelling, survival, testthat

Extension of 'ggplot2', 'ggstatsplot' creates
graphics with details from statistical tests included in the plots
themselves. It is targeted primarily at behavioral sciences community
to provide a one-line code to generate information-rich plots for
statistical analysis of continuous (violin plots, scatterplots,
histograms, dot plots, dot-and-whisker plots) or categorical (pie and
bar charts) data. Currently, it supports only the most common types of
statistical tests: parametric, nonparametric, robust, and bayesian
versions of t-test/anova, correlation analyses, contingency table
analysis, meta-analysis, and regression analyses.
broomExtra, cowplot, dplyr, ggcorrplot, ggExtra, ggplot2,
ggrepel, ggsignif, groupedstats, insight, ipmisc,
pairwiseComparisons, paletteer, purrr, rlang, stats,
statsExpressions, tidyr
forcats, knitr, lme4, MASS, MCMCglmm, ordinal, rmarkdown,
spelling, survival, testthat, tibble, WRS2