# Smooth Survival Models, Including Generalized Survival Models

## Documentation for package ‘rstpm2’ version 1.5.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

Rstpm2-package | Flexible parametric survival models.
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.
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.stpm2 | S3 method for to provide exponentiated coefficents with confidence intervals.
grad | gradient function (internal function)
gsm | Parametric and penalised generalised survival models
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 "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.
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.
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.
print.predictnl | 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.
Rstpm2 | Flexible parametric survival models.
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.
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"'
vcov.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models.
vuniroot | Vectorised One Dimensional Root (Zero) Finding
zeroModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models.