contsurvplot: Visualize the Effect of a Continuous Variable on a Time-to-Event Outcome

Graphically display the (causal) effect of a continuous variable on a time-to-event outcome using multiple different types of plots based on g-computation. Those functions include, among others, survival area plots, survival contour plots, survival quantile plots and 3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally. For details, see Robin Denz, Nina Timmesfeld (2022) <arXiv:2208.04644v1>.

Version: 0.2.0
Imports: ggplot2 (≥ 3.4.0), dplyr, rlang, riskRegression, foreach
Suggests: survival, pammtools, gganimate, transformr, plotly, reshape2, doParallel, knitr, rmarkdown, testthat (≥ 3.0.0), vdiffr (≥ 1.0.0), covr
Published: 2023-01-05
Author: Robin Denz [aut, cre]
Maintainer: Robin Denz <robin.denz at>
Contact: <>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: contsurvplot citation info
Materials: README NEWS
CRAN checks: contsurvplot results


Reference manual: contsurvplot.pdf
Vignettes: Visualizing the Causal Effect of a Continuous Variable on a Time-To-Event Outcome


Package source: contsurvplot_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): contsurvplot_0.2.0.tgz, r-oldrel (arm64): contsurvplot_0.2.0.tgz, r-release (x86_64): contsurvplot_0.2.0.tgz, r-oldrel (x86_64): contsurvplot_0.2.0.tgz
Old sources: contsurvplot archive


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