broomExtra, cowplot, dplyr, ggcorrplot, ggExtra, ggplot2 (>=
3.3.0), ggrepel, ggsignif, groupedstats, insight, ipmisc,
pairwiseComparisons, paletteer, purrr, rlang, stats,
statsExpressions, tidyr
forcats, knitr, lme4, MASS, ordinal, rmarkdown, spelling,
survival, testthat, tibble, WRS2

c(person(given = "Indrajeet",
family = "Patil",
role = c("cre", "aut", "cph"),
email = "patilindrajeet.science@gmail.com",
comment = c(ORCID = "0000-0003-1995-6531")),
person(given = "Chuck",
family = "Powell",
role = "ctb",
email = "ibecav@gmail.com",
comment = c(ORCID = "0000-0002-3606-2188")))
broomExtra (>= 3.0.0), correlation (>= 0.2.0), cowplot, dplyr,
ggcorrplot, ggExtra, ggplot2, ggrepel, ggsignif, groupedstats,
insight, ipmisc (>= 2.0.0), pairwiseComparisons (>= 0.3.0),
paletteer, purrr, rlang, stats, statsExpressions (>= 0.4.0),
tidyr
Indrajeet Patil [cre, aut, cph]
(<https://orcid.org/0000-0003-1995-6531>),
Chuck Powell [ctb] (<https://orcid.org/0000-0002-3606-2188>)

c(person(given = "Indrajeet",
family = "Patil",
role = c("cre", "aut", "cph"),
email = "patilindrajeet.science@gmail.com",
comment = c(ORCID = "0000-0003-1995-6531")))
broomExtra, correlation, cowplot, dplyr (>= 1.0.0),
ggcorrplot, ggExtra, ggplot2, ggrepel, ggsignif, groupedstats
(>= 1.0.1), insight, ipmisc, pairwiseComparisons (>= 1.0.0),
paletteer, parameters, purrr, rlang, stats, statsExpressions,
tidyr
forcats, knitr, lme4, MASS, rmarkdown, spelling, testthat,
tibble, WRS2
Indrajeet Patil [cre, aut, cph]
(<https://orcid.org/0000-0003-1995-6531>)

person(given = "Indrajeet",
family = "Patil",
role = c("cre", "aut", "cph"),
email = "patilindrajeet.science@gmail.com",
comment = c(ORCID = "0000-0003-1995-6531"))
Extension of 'ggplot2', 'ggstatsplot' creates
graphics with details from statistical tests included in the plots
themselves. It provides easier API 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 (>= 4.0.5), cowplot, dplyr, effectsize, ggcorrplot,
ggExtra, ggplot2, ggrepel, ggsignif, insight, ipmisc,
pairwiseComparisons (>= 2.0.1), paletteer, parameters, purrr,
rlang, stats, statsExpressions (>= 0.5.0), tidyr (>= 0.3.0)
forcats, knitr, MASS, rmarkdown, spelling, testthat, tibble,
WRS2