person("Trung Kien", "Dang", role = "ctb", comment = c(ORCID = "0000-0001-7562-6495")),
person("Pierre", "Gramme", role = "ctb"),
person("Amir Masoud", "Abdol", role = "ctb"),
person("Malcolm", "Barrett", role = "ctb", comment = c(ORCID = "0000-0003-0299-5825")),
person("Robrecht", "Cannoodt", role = "ctb", comment = c(ORCID = "0000-0003-3641-729X")),
Imports: grid, Rcpp, rlang (>= 0.3.0), scales (>= 0.5.0)
Suggests: knitr, rmarkdown, testthat, gridExtra, devtools, prettydoc,
ggbeeswarm, dplyr, magrittr, readr, stringr

person("Michał", "Krassowski", role = "ctb", comment = c(ORCID = "0000-0002-9638-7785")),
person("Michael", "Chirico", role = "ctb", comment = c(ORCID = "0000-0003-0787-087X")),
person("Pedro", "Aphalo", role = "ctb", comment = c(ORCID = "0000-0003-3385-972X"))

Suggests: knitr, rmarkdown, testthat, svglite, vdiffr, gridExtra,
devtools, prettydoc, ggbeeswarm, dplyr, magrittr, readr,

person("RStudio", role = c("cph"), comment = "Copyright for ggplot2 code copied to ggridges"))
Description: Ridgeline plots provide a convenient way of visualizing changes in distributions over
Collate: 'data.R' 'ggridges.R' 'geoms.R' 'geomsv.R' 'geoms-gradient.R'
RStudio [cph] (Copyright for ggplot2 code copied to ggridges)

Description: Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.

Suggests: covr, dplyr, gridExtra, ggplot2movies, DAAG, forcats, knitr,

Suggests: covr, dplyr, patchwork, ggplot2movies, forcats, knitr,
Packaged: 2020-01-11 23:38:10 UTC; clauswilke

Packaged: 2021-01-07 02:01:43 UTC; clauswilke

Packaged: 2022-09-22 21:53:29 UTC; clauswilke

Title: Risk Score Plot for Cox Regression
Description: The risk plot may be one of the most commonly used figures in
tumor genetic data analysis. We can conclude the following two points:
Comparing the prediction results of the model with the real survival situation
to see whether the survival rate of the high-risk group is lower than that of the
low-level group, and whether the survival time of the high-risk group is
shorter than that of the low-risk group. The other is to compare the heat
map and scatter plot to see the correlation between the predictors and the
Imports: ggplot2, survival, egg, do, set, cutoff, fastStat, grid, rms,