survivalSL1.0 package

Super Learner for Survival Prediction from Censored Data

LIB_AFTgamma

Library of the Super Learner for an Accelerated Failure Time (AFT) Mod...

LIB_AFTggamma

Library of the Super Learner for an Accelerated Failure Time (AFT) Mod...

LIB_AFTllogis

Library of the Super Learner for an Accelerated Failure Time (AFT) Mod...

LIB_AFTweibull

Library of the Super Learner for an Accelerated Failure Time (AFT) Mod...

LIB_COXaic

Library of the Super Learner for a Cox Model with Selected Covariates

LIB_COXall

Library of the Super Learner for Cox Regression

LIB_COXen

Library of the Super Learner for Elastic Net Cox Regression

LIB_COXlasso

Library of the Super Learner for Lasso Cox Regression

LIB_COXridge

Library of the Super Learner for Ridge Cox Regression

LIB_PHexponential

Library of the Super Learner for a Proportional Hazards (PH) Model wit...

LIB_PHgompertz

Library of the Super Learner for an Proportional Hazards (PH) Model wi...

LIB_PHspline

Library of the Super Learner for an Survival Regression using the Roys...

LIB_PLANN

Library of the Super Learner for Survival Neural Network Based on the ...

LIB_RSF

Library of the Super Learner for Survival Random Survival Forest

metrics

Metrics to Evaluate the Prognostic Capacities

plot.libsl

Calibration Plot

plot.sltime

Calibration Plot for Super Learner

predict.libsl

Prediction from an Flexible Parametric Model

predict.sltime

Prediction from a Super Learner for Censored Outcomes

print.libsl

S3 Method for Printing an 'libsl' Object

print.sltime

S3 Method for Printing an 'sltime' Object

summary.libsl

Summaries of a Learner

summary.sltime

Summaries of a Super Learner

survivalSL

Super Learner for Censored Outcomes

tuneCOXen

Tune Elastic Net Cox Regression

tuneCOXlasso

Tune Lasso Cox Regression

tuneCOXridge

Tune Ridge Cox Regression

tunePHspline

Tune a Survival Regression using the Royston/Parmar Spline Model

tunePLANN

Tune a Survival Neural Network Based on the PLANN Method

tuneRSF

Tune a Survival Random Forest

Several functions and S3 methods to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities.

  • Maintainer: Yohann Foucher
  • License: GPL (>= 2)
  • Last published: 2025-12-16