causalCmprsk: Nonparametric and Cox-Based Estimation of ATE in Competing Risks

Estimation of average treatment effects (ATE) of two static treatment regimes on time-to-event outcomes with K competing events (K can be 1). The method uses propensity scores weighting for emulation of baseline randomization.

Version: 1.0.1
Depends: R (≥ 3.6)
Imports: survival, inline, doParallel, parallel, utils, foreach, data.table, purrr
Suggests: knitr, rmarkdown, bookdown, tidyverse, ggalt, cobalt, ggsci, modEvA, naniar, DT, Hmisc, hrbrthemes, summarytools, kableExtra
Published: 2021-01-05
Author: Bella Vakulenko-Lagun, Colin Magdamo, Marie-Laure Charpignon, Bang Zheng, Mark Albers, Sudeshna Das
Maintainer: Bella Vakulenko-Lagun <blagun at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: causalCmprsk results


Reference manual: causalCmprsk.pdf
Vignettes: Nonparametric and Cox-based estimation of ATE in competing risks
Package source: causalCmprsk_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): causalCmprsk_1.0.1.tgz, r-release (x86_64): causalCmprsk_1.0.1.tgz, r-oldrel: causalCmprsk_1.0.1.tgz
Old sources: causalCmprsk archive


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