| addModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| aft | Parametric accelerated failure time model with smooth time functions |
| aft-class | Class "stpm2" ~~~ |
| aftModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| AIC-method | Class "pstpm2" |
| AICc-method | Class "pstpm2" |
| anova-method | Class "pstpm2" |
| as.data.frame.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| as.data.frame.markov_msm_diff | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| as.data.frame.markov_msm_ratio | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| as.data.frame.markov_sde | Predictions for continuous time, nonhomogeneous Markov multi-state models using Aalen's additive hazards models. |
| bhazard | Placemarker function for a baseline hazard function. |
| BIC-method | Class "pstpm2" |
| brcancer | German breast cancer data from Stata. |
| coef<- | Generic method to update the coef in an object. |
| collapse_markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| colon | Colon cancer. |
| confint.predictnl | Estimation of standard errors using the numerical delta method. |
| cox.tvc | Test for a time-varying effect in the 'coxph' model |
| diff | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| diff.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| eform | S3 method for to provide exponentiated coefficents with confidence intervals. |
| eform-method | Class "pstpm2" |
| eform-method | Class "stpm2" ~~~ |
| eform.default | S3 method for to provide exponentiated coefficents with confidence intervals. |
| eform.stpm2 | S3 method for to provide exponentiated coefficents with confidence intervals. |
| grad | gradient function (internal function) |
| gsm | Parametric and penalised generalised survival models |
| gsm.control | Defaults for the gsm call |
| gsm_design | Extract design information from an stpm2/gsm object and newdata for use in C++ |
| hazFun | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| hrModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| incrVar | Utility that returns a function to increment a variable in a data-frame. |
| legendre.quadrature.rule.200 | Legendre quadrature rule for n=200. |
| lhs | Internal functions for the rstpm2 package. |
| lhs<- | Internal functions for the rstpm2 package. |
| lines-method | Class "stpm2" ~~~ |
| lines-method | Class "pstpm2" |
| lines-method | Class "stpm2" ~~~ |
| lines.pstpm2 | S3 methods for lines |
| lines.stpm2 | S3 methods for lines |
| markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| markov_sde | Predictions for continuous time, nonhomogeneous Markov multi-state models using Aalen's additive hazards models. |
| nsx | Generate a Basis Matrix for Natural Cubic Splines (with eXtensions) |
| nsxD | Generate a Basis Matrix for the first derivative of Natural Cubic Splines (with eXtensions) |
| numDeltaMethod | Calculate numerical delta method for non-linear predictions. |
| plot-method | Class "stpm2" ~~~ |
| plot-method | plots for an stpm2 fit |
| plot-method | Class "pstpm2" |
| plot-method | Class "stpm2" ~~~ |
| plot-method | Class '"tvcCoxph"' |
| plot-methods | plots for an stpm2 fit |
| plot.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| plot.markov_sde | Predictions for continuous time, nonhomogeneous Markov multi-state models using Aalen's additive hazards models. |
| popmort | Background mortality rates for the colon dataset. |
| predict-method | Class "stpm2" ~~~ |
| predict-method | Predicted values for an stpm2 or pstpm2 fit |
| predict-methods | Predicted values for an stpm2 or pstpm2 fit |
| predict.formula | Estimation of standard errors using the numerical delta method. |
| predict.nsx | Evaluate a Spline Basis |
| predictnl | Estimation of standard errors using the numerical delta method. |
| predictnl-method | Class "stpm2" ~~~ |
| predictnl-method | ~~ Methods for Function predictnl ~~ |
| predictnl-method | Class "pstpm2" |
| predictnl-method | Class "stpm2" ~~~ |
| predictnl-methods | ~~ Methods for Function predictnl ~~ |
| predictnl.default | Estimation of standard errors using the numerical delta method. |
| predictnl.lm | Estimation of standard errors using the numerical delta method. |
| pstpm2 | Parametric and penalised generalised survival models |
| pstpm2-class | Class "pstpm2" |
| qAICc-method | Class "pstpm2" |
| ratio_markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| rbind.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| residuals-method | Residual values for an stpm2 or pstpm2 fit |
| residuals-methods | Residual values for an stpm2 or pstpm2 fit |
| rhs | Internal functions for the rstpm2 package. |
| rhs<- | Internal functions for the rstpm2 package. |
| simulate-method | Simulate values from an stpm2 or pstpm2 fit |
| simulate-methods | Simulate values from an stpm2 or pstpm2 fit |
| smoothpwc | Utility to use a smooth function in markov_msm based on piece-wise constant values |
| splineFun | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| standardise | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| standardise.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| standardise.markov_sde | Predictions for continuous time, nonhomogeneous Markov multi-state models using Aalen's additive hazards models. |
| stpm2 | Parametric and penalised generalised survival models |
| stpm2-class | Class "stpm2" ~~~ |
| subset.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| summary-method | Class "pstpm2" |
| summary-method | Class "stpm2" ~~~ |
| transform.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| tvcCoxph-class | Class '"tvcCoxph"' |
| update-method | Methods for Function update |
| update-methods | Methods for Function update |
| vcov.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| voptimise | Vectorised One Dimensional Optimization |
| voptimize | Vectorised One Dimensional Optimization |
| vuniroot | Vectorised One Dimensional Root (Zero) Finding |
| zeroModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |