# Smooth Survival Models, Including Generalized Survival Models

## Documentation for package ‘rstpm2’ version 1.6.1

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

## Help Pages

A B C D E G H I L M N P Q R S T U V Z

Rstpm2-package | Flexible parametric survival models.

## -- A --

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.
aft_integrated | Parametric accelerated failure time model with smooth time functions
aft_integrated-class | Class "stpm2" ~~~
aft_mixture | Parametric accelerated failure time model with smooth time functions
aft_mixture-class | Class "stpm2" ~~~
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.

## -- B --

bhazard | Placemarker function for a baseline hazard function.
BIC-method | Class "pstpm2"
brcancer | German breast cancer data from Stata.

## -- C --

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

## -- D --

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.

## -- E --

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.

## -- G --

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++

## -- H --

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.

## -- I --

incrVar | Utility that returns a function to increment a variable in a data-frame.

## -- L --

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

## -- M --

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.

## -- N --

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.

## -- P --

plot-method | Class "stpm2" ~~~
plot-method | Class "stpm2" ~~~
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 | Class "stpm2" ~~~
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 | Class "stpm2" ~~~
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"

## -- Q --

qAICc-method | Class "pstpm2"

## -- R --

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.

## -- S --

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" ~~~

## -- T --

transform.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models.
tvcCoxph-class | Class '"tvcCoxph"'

## -- U --

update-method | Methods for Function update
update-methods | Methods for Function update

## -- V --

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

## -- Z --

zeroModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models.