| aft.gamma |
Library of the Super Learner for an accelerated failure time (AFT) parametric model with a gamma distribution |
| aft.ggamma |
Library of the Super Learner for an accelerated failure time (AFT) parametric model with a generalized gamma distribution |
| aft.llogis |
Library of the Super Learner for an accelerated failure time (AFT) parametric model with a log logistic distribution |
| aft.weibull |
Library of the Super Learner for an accelerated failure time (AFT) parametric model with a Weibull distribution |
| auc |
Area Under ROC Curve From Sensitivities And Specificities. |
| cox.aic |
Library of the Super Learner for Cox univariate significant model |
| cox.all |
Library of the Super Learner for Cox Regression |
| cox.en |
Library of the Super Learner for Elastic Net Cox Regression |
| cox.lasso |
Library of the Super Learner for Lasso Cox Regression |
| cox.ridge |
Library of the Super Learner for Ridge Cox Regression |
| dataCSL |
CSL Liver Chirrosis Data. |
| dataDIVAT1 |
A First Sample From The DIVAT Data Bank. |
| dataDIVAT2 |
A Second Sample From the DIVAT Data Bank. |
| dataDIVAT3 |
A Third Sample From the DIVAT Data Bank. |
| dataDIVAT4 |
A Fourth Sample From the DIVAT Data Bank. |
| dataDIVAT5 |
The Aggregated Kidney Graft Survival Stratified By The 1-year Serum Creatinine. |
| dataFTR |
Data for First Kidney Transplant Recipients. |
| dataHepatology |
The Data Extracted From The Meta-Analysis By Cabibbo et al. (2010). |
| dataKi67 |
The Aggregated Data Published By de Azambuja et al. (2007). |
| dataKTFS |
A Sixth Sample Of The DIVAT Cohort. |
| dataOFSEP |
A Simulated Sample From the OFSEP Cohort. |
| dataSTR |
Data for Second Kidney Transplant Recipients. |
| differentiation |
Numerical Differentiation with Finite Differences. |
| expect.utility1 |
Cut-Off Estimation Of A Prognostic Marker (Only One Observed Group). |
| expect.utility2 |
Cut-Off Estimation Of A Prognostic Marker (Two Groups Are observed). |
| fr.ratetable |
Expected Mortality Rates of the General French Population |
| gc.logistic |
Marginal Effect for Binary Outcome by G-computation. |
| gc.sl.binary |
Marginal Effect for Binary Outcome by Super Learned G-computation. |
| gc.sl.time |
Marginal Effect for Censored Outcome by G-computation with a Super Learner for the Outcome Model. |
| gc.survival |
Marginal Effect for Censored Outcome by G-computation with a Cox Regression for the Outcome Model. |
| hr.sl.time |
Conditionnal Effect for Censored Outcome with a Super Learner for the Outcome Model. |
| ipw.log.rank |
Log-Rank Test for Adjusted Survival Curves. |
| ipw.survival |
Adjusted Survival Curves by Using IPW. |
| lines.rocrisca |
Add Lines to a ROC Plot |
| lrs.multistate |
Likelihood Ratio Statistic to Compare Embedded Multistate Models |
| markov.3states |
3-State Time-Inhomogeneous Markov Model |
| markov.3states.rsadd |
3-state Relative Survival Markov Model with Additive Risks |
| markov.4states |
4-State Time-Inhomogeneous Markov Model |
| markov.4states.rsadd |
4-state Relative Survival Markov Model with Additive Risks |
| metric |
Metrics to Evaluate the Prognostic Capacities |
| mixture.2states |
Horizontal Mixture Model for Two Competing Events |
| nnet.time |
Library of the Super Learner for Survival Neural Network |
| ph.exponential |
Library of the Super Learner for an proportional hazards (PH) parametric model with an Exponential distribution |
| ph.gompertz |
Library of the Super Learner for an proportional hazards (PH) parametric model with a Gompertz distribution |
| plot.rocrisca |
Plot Method for 'rocrisca' Objects |
| plot.sl.time |
Caliration Plot for Super Learner |
| plot.survrisca |
Plot Method for 'survrisca' Objects |
| port |
POsitivity-Regression Tree (PoRT) Algorithm to Identify Positivity Violations. |
| pred.mixture.2states |
Cumulative Incidence Function Form Horizontal Mixture Model With Two Competing Events |
| predict.cox |
Prediction from a Penalized Cox Regression |
| predict.flexsurv |
Prediction from an Flexible Parametric Model |
| predict.nnet.time |
Prediction from a Survival Neural Network |
| predict.rf.time |
Prediction from a Suvival Random Forest Tree |
| predict.sl.time |
Prediction from an Super Learner (SL) for Censored Outcomes |
| rf.time |
Library of the Super Learner for Survival Random Forest Tree |
| rmst |
Restricted Mean Survival Times. |
| roc.binary |
ROC Curves For Binary Outcomes. |
| roc.net |
Net Time-Dependent ROC Curves With Right Censored Data. |
| roc.prognostic.aggregate |
Prognostic ROC Curve Based on Survival Probabilities |
| roc.prognostic.individual |
Prognostic ROC Curve based on Individual Data |
| roc.summary |
Summary ROC Curve For Aggregated Data. |
| roc.time |
Time-Dependent ROC Curves With Right Censored Data. |
| semi.markov.3states |
3-State Semi-Markov Model |
| semi.markov.3states.ic |
3-State Semi-Markov Model With Interval-Censored Data |
| semi.markov.3states.rsadd |
3-State Relative Survival Semi-Markov Model With Additive Risks |
| semi.markov.4states |
4-State Semi-Markov Model |
| semi.markov.4states.rsadd |
4-State Relative Survival Semi-Markov Model With Additive Risks |
| sl.time |
Super Learner for Censored Outcomes |
| summary.sl.time |
Summaries of a Super Learner |
| survival.mr |
Multiplicative-Regression Model to Compare the Risk Factors Between Two Reference and Relative Populations |
| survival.summary |
Summary Survival Curve From Aggregated Data |
| survival.summary.strata |
Summary Survival Curve And Comparison Between Strata. |
| tune.cox.aic |
Tune cox step AIC with forward selection |
| tune.cox.en |
Tune Elastic Net Cox Regression |
| tune.cox.lasso |
Tune Lasso Cox Regression |
| tune.cox.ridge |
Tune Ridge Cox Regression |
| tune.nnet.time |
Tune a 1-Layer Survival Neural Network |
| tune.rf.time |
Tune Survival Random Forest Tree |
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