Tools for Flexible Survival Analysis Using Machine Learning R package survML version 1.2.0 Statistical tools for analyzing time-to-event data using machine learning. Implements survival stacking for conditional survival estimation, standardized survival function estimation for current status data, and methods for algorithm-agnostic variable importance. See Wolock CJ, Gilbert PB, Simon N, and Carone M (2024) <doi:10.1080/10618600.2024.2304070>. Software Depends: SuperLearner (>= 2.0.28), Imports: Iso (>= 0.0.18.1), haldensify (>= 0.2.3), fdrtool (>= 1.2.17), ChernoffDist (>= 0.1.0), dplyr (>= 1.0.10), gtools (>= 3.9.5), mboost (>= 2.9.0), survival (>= 3.5.0), stats (>= 4.3.2), methods (>= 4.3.2) Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2 (>= 3.4.0), gam (>= 1.22.0) Charles Wolock <cwolock@gmail.com> Comprehensive R Archive Network (CRAN) Charles Wolock [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-3527-1102>), Avi Kenny [ctb] (ORCID: <https://orcid.org/0000-0002-9465-7307>) GPL (>= 3) 2024-10-31 application/tgz https://CRAN.R-project.org/package=survML doi:10.32614/CRAN.package.survML