Package: survML 1.2.0.9000
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>.
Authors:
survML_1.2.0.9000.tar.gz
survML_1.2.0.9000.zip(r-4.5)survML_1.2.0.9000.zip(r-4.4)survML_1.2.0.9000.zip(r-4.3)
survML_1.2.0.9000.tgz(r-4.5-any)survML_1.2.0.9000.tgz(r-4.4-any)survML_1.2.0.9000.tgz(r-4.3-any)
survML_1.2.0.9000.tar.gz(r-4.5-noble)survML_1.2.0.9000.tar.gz(r-4.4-noble)
survML_1.2.0.9000.tgz(r-4.4-emscripten)survML_1.2.0.9000.tgz(r-4.3-emscripten)
survML.pdf |survML.html✨
survML/json (API)
NEWS
| # Install 'survML' in R: |
| install.packages('survML', repos = c('https://cwolock.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/cwolock/survml/issues
Pkgdown site:https://cwolock.github.io
Last updated 3 hours agofrom:a4a04d7c79. Checks:9 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Apr 01 2025 |
| R-4.5-win | OK | Apr 01 2025 |
| R-4.5-mac | OK | Apr 01 2025 |
| R-4.5-linux | OK | Apr 01 2025 |
| R-4.4-win | OK | Apr 01 2025 |
| R-4.4-mac | OK | Apr 01 2025 |
| R-4.4-linux | OK | Apr 01 2025 |
| R-4.3-win | OK | Apr 01 2025 |
| R-4.3-mac | OK | Apr 01 2025 |
Exports:crossfit_oracle_predscrossfit_surv_predscurrstatCIRDR_pseudo_outcome_regressiongenerate_foldsstackGstackLvimvim_accuracyvim_AUCvim_briervim_cindexvim_rsquaredvim_survival_time_mse
Dependencies:abindADGofTestassertthatbitopscaToolsChernoffDistclicodetoolscolorspacecopulacpp11cvAUCdata.tabledigestdplyrfansifarverfdrtoolforeachFormulafurrrfuturefuture.applygamgenericsggplot2glmnetglobalsgluegplotsgslgtablegtoolshal9001haldensifyinumIsoisobanditeratorsKernSmoothlabelinglatticelibcoinlifecyclelistenvmagrittrMASSMatrixmatrixStatsmboostmgcvmunsellmvtnormnlmennlsnumDerivorigamiparallellypartykitpcaPPpillarpkgconfigpsplinepurrrquadprogR6rbibutilsRColorBrewerRcppRcppEigenRdpackrlangROCRrpartrsamplescalesshapesliderstablediststabsstringistringrSuperLearnersurvivaltibbletidyrtidyselectutf8vctrsviridisLitewarpwithr
Assessing variable importance in survival analysis using machine learning
Rendered fromvariable_importance.Rmdusingknitr::rmarkdownon Apr 01 2025.Last update: 2025-03-21
Started: 2024-10-26
Estimating a conditional survival function using off-the-shelf machine learning tools
Rendered fromconditional_survival.Rmdusingknitr::rmarkdownon Apr 01 2025.Last update: 2024-10-28
Started: 2024-05-21
Estimating a covariate-adjusted survival function using current status data
Rendered fromcurrent_status.Rmdusingknitr::rmarkdownon Apr 01 2025.Last update: 2025-03-18
Started: 2024-10-28
