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.

Authors:Charles Wolock [aut, cre, cph], Avi Kenny [ctb]

survML_1.2.0.9000.tar.gz
survML_1.2.0.9000.zip(r-4.7)survML_1.2.0.9000.zip(r-4.6)survML_1.2.0.9000.zip(r-4.5)
survML_1.2.0.9000.tgz(r-4.6-any)survML_1.2.0.9000.tgz(r-4.5-any)
survML_1.2.0.9000.tar.gz(r-4.7-any)survML_1.2.0.9000.tar.gz(r-4.6-any)
survML_1.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
survML/json (API)

# 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/docs site:https://cwolock.github.io

On CRAN:

Conda:

7.94 score 18 stars 1 packages 92 scripts 584 downloads 21 exports 83 dependencies

Last updated from:bb9a5793f7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK209
source / vignettesOK356
linux-release-x86_64OK325
macos-release-arm64OK208
macos-oldrel-arm64OK255
windows-develOK189
windows-releaseOK171
windows-oldrelOK159
wasm-releaseOK125

Exports:aggregate_vimboost_c_indexcrossfit_oracle_predscrossfit_surv_predscurrstatCIRDR_pseudo_outcome_regressiongenerate_foldsgenerate_nuisance_predictions_stackGgenerate_oracle_predictions_boostgenerate_oracle_predictions_DRgenerate_oracle_predictions_SLmultiseed_vimstackGstackLvimvim_accuracyvim_AUCvim_briervim_cindexvim_rsquaredvim_survival_time_mse

Dependencies:abindADGofTestassertthatbitopscaToolsChernoffDistcliclustercodetoolscolorspacecopulacpp11cvAUCdata.tabledigestdplyrfarverfdrtoolforeachFormulafuturefuture.applygamgenericsggplot2glmnetglobalsgluegplotsgslgtablegtoolshal9001haldensifyinumIsoisobanditeratorsKernSmoothlabelinglatticelibcoinlifecyclelistenvmagrittrMatrixmatrixStatsmboostmvtnormnnlsnumDerivorigamiparallellypartykitpcaPPpillarpkgconfigpsplinequadprogR6rbibutilsRColorBrewerRcppRcppEigenRdpackrlangROCRrpartS7scalesshapestablediststabsstringistringrSuperLearnersurvivaltibbletidyselectutf8vctrsviridisLitewithr

Covariate-adjusted survival curves with current status data
Introduction | Current status data structure | Extended CIR method | References
Rendered fromv5_current_status.Rmdusingknitr::rmarkdown

Last update: 2025-07-29
Started: 2025-06-08

Variable importance in survival analysis: maximizing C-index
Introduction | Boosting the C-index | Example: Predicting recurrence-free survival time in cancer patients | References
Rendered fromv4_cindex_boosting.Rmdusingknitr::rmarkdown

Last update: 2025-06-09
Started: 2025-06-08

Variable importance in survival analysis: multi-seed estimation
Introduction | Muli-seed VIM estimation | Point estimates | Inference | Example: Predicting recurrence-free survival time in cancer patients | References
Rendered fromv3_sample_splitting.Rmdusingknitr::rmarkdown

Last update: 2025-06-09
Started: 2025-06-05

Variable importance in survival analysis: overview
Introduction | Example: Predicting recurrence-free survival time in cancer patients | Estimating variable importance relative to all features | Estimating variable importance relative to base model | Adjustment variables | Writing a custom generator function | Appendix | References
Rendered fromv2_variable_importance.Rmdusingknitr::rmarkdown

Last update: 2025-06-09
Started: 2025-06-05

Conditional survival function estimation
Introduction | Global survival stacking | Example | Local survival stacking | References
Rendered fromv1_conditional_survival.Rmdusingknitr::rmarkdown

Last update: 2025-06-06
Started: 2025-06-05

Readme and manuals

Help Manual

Help pageTopics
Aggregate multiseed VIM resultsaggregate_vim
Gradient boosting for C-indexboost_c_index
Generate cross-fitted oracle prediction function estimatescrossfit_oracle_preds
Generate cross-fitted conditional survival predictionscrossfit_surv_preds
Estimate a survival function under current status samplingcurrstatCIR
Doubly-robust pseudo-outcome regressionDR_pseudo_outcome_regression
Generate cross-fitting and sample-splitting foldsgenerate_folds
Estimate conditional survival function nuisance parameters using survival stackinggenerate_nuisance_predictions_stackG
Estimate oracle prediction function using DR gradient boostinggenerate_oracle_predictions_boost
Estimate full oracle prediction function using DR pseudo-outcome regressiongenerate_oracle_predictions_DR
Estimate small oracle prediction function using Super Learner regressiongenerate_oracle_predictions_SL
Estimate variable importance with multiple seedsmultiseed_vim
Obtain predicted conditional survival and cumulative hazard functions from a global survival stacking objectpredict.stackG
Obtain predicted conditional survival function from a local survival stacking objectpredict.stackL
Estimate a conditional survival function using global survival stackingstackG
Estimate a conditional survival function via local survival stackingstackL
Estimate variable importancevim
Estimate classification accuracy VIMvim_accuracy
Estimate AUC VIMvim_AUC
Estimate Brier score VIMvim_brier
Estimate concordance index VIMvim_cindex
Estimate R-squared (proportion of explained variance) VIM based on event occurrence by a landmark timevim_rsquared
Estimate restricted predicted survival time MSE VIMvim_survival_time_mse