survML: Tools for Flexible Survival Analysis Using Machine Learning

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>.

Package overview README.md Assessing variable importance in survival analysis using machine learning Estimating a conditional survival function using off-the-shelf machine learning tools Estimating a covariate-adjusted survival function using current status data

Vignettes Man pages API and functions Files

Package details
Author	Charles Wolock [aut, cre, cph] (<https://orcid.org/0000-0003-3527-1102>), Avi Kenny [ctb] (<https://orcid.org/0000-0002-9465-7307>)
Maintainer	Charles Wolock <cwolock@gmail.com>
License	GPL (>= 3)
Version	1.2.0
URL	https://github.com/cwolock/survML https://cwolock.github.io/survML/
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("survML")`