backports, data.table (>= 1.10.4), fpCompare, ggplot2,
grDevices, grid, gridBase, igraph, methods, raster,
RColorBrewer, rgdal, rgeos, sp, stats

A high-level plotting system, built using 'grid' graphics, that is
optimized for speed and modularity. This has great utility for quick
visualizations when testing code, with the key benefit that visualizations
are updated independently of one another.
The suggested packages 'fastshp' and 'visualTest' can be installed with e.g.,
'install.packages("fastshp", repos = "https://PredictiveEcology.r-universe.dev")'.
c(
person("Eliot J B", "McIntire", email = "eliot.mcintire@canada.ca",
role = c("aut", "cre"), comment=c(ORCID = "https://orcid.org/0000-0002-6914-8316")),
person("Alex M", "Chubaty", email = "achubaty@for-cast.ca",
role = c("aut"), comment = c(ORCID = "https://orcid.org/0000-0001-7146-8135")),
person("Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada",
role = "cph")
)
covr, fastshp, knitr, png, rmarkdown, spelling, testthat (>=
1.0.2), visualTest
'environment.R' 'plotting-classes.R' 'plotting-colours.R'
'plotting-helpers.R' 'plotting-other.R' 'plotting.R'
'quickPlot-classes.R' 'quickPlot-package.R' 'testing-helpers.R'
'zzz.R'

Build Regression Models Quickly and Display the Results Using
'ggplot2'
person("Xikun", "Han", email = "hanxikun2014@163.com",role = c("aut", "cre"))
A set of functions to extract results from regression models and
plot the effect size using 'ggplot2' seamlessly. While 'broom' is useful to
convert statistical analysis objects into tidy data frames, 'coefplot' is adept at showing
multivariate regression results. With specific outcome, this package could build regression models
automatically, extract results into a data frame and provide a quicker way to summarize
models' statistical findings using 'ggplot2'.
ggplot2 (>= 2.0.0), survival, psych, utils, stats

ggplot2 (>= 2.0.0), survival, psych, utils, stats, nortest

ggplot2 (>= 2.0.0), rlang (>= 0.1.2), survival, psych, utils,
stats, nortest, dplyr