Nonparametric and Cox-Based Estimation of Average Treatment Effects in Competing Risks R package causalCmprsk version 2.0.0 Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>. Software Depends: R (>= 4.0.0) Imports: survival, inline, doParallel, parallel, utils, foreach, data.table, purrr, methods Suggests: knitr, rmarkdown, bookdown, tidyverse, ggalt, cobalt, ggsci, modEvA, naniar, DT, Hmisc, hrbrthemes, summarytools Bella Vakulenko-Lagun <blagun@stat.haifa.ac.il> Comprehensive R Archive Network (CRAN) Bella Vakulenko-Lagun [aut, cre], Colin Magdamo [aut], Marie-Laure Charpignon [aut], Bang Zheng [aut], Mark Albers [aut], Sudeshna Das [aut] GPL (>= 2) 2023-07-04 application/tgz https://CRAN.R-project.org/package=causalCmprsk doi:10.32614/CRAN.package.causalCmprsk