totlos.fs
(Package: flexsurv) :
Total length of stay in particular states for a fully-parametric,
The matrix whose r,s entry is the expected amount of time spent in state s for a time-inhomogeneous, continuous-time Markov multi-state process that starts in state r, up to a maximum time t. This is defined as the integral of the corresponding transition probability up to that time.
GenF
(Package: flexsurv) :
Generalized F distribution
Density, distribution function, hazards, quantile function and random generation for the generalized F distribution, using the reparameterisation by Prentice (1975).
● Data Source:
CranContrib
● Keywords: distribution
● Alias: GenF, Hgenf, dgenf, pgenf, qgenf, rgenf
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0 images
Compute a basis for a natural cubic spline, using the parameterisation described by Royston and Parmar (2002). Used for flexible parametric survival models.
GenGamma.orig
(Package: flexsurv) :
Generalized gamma distribution (original parameterisation)
Density, distribution function, hazards, quantile function and random generation for the generalized gamma distribution, using the original parameterisation from Stacy (1962).
● Data Source:
CranContrib
● Keywords: distribution
● Alias: GenGamma.orig, Hgengamma.orig, dgengamma.orig, pgengamma.orig, qgengamma.orig, rgengamma.orig
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0 images
sim.fmsm
(Package: flexsurv) :
Simulate paths through a fully parametric semi-Markov multi-state model
Simulate changes of state and transition times from a semi-Markov multi-state model fitted using flexsurvreg.
Density, distribution function, hazards, quantile function and random generation for the Gompertz distribution with unrestricted shape.
● Data Source:
CranContrib
● Keywords: distribution
● Alias: Gompertz, dgompertz, hgompertz, pgompertz, qgompertz, rgompertz
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0 images
pmatrix.simfs
(Package: flexsurv) :
Transition probability matrix from a fully-parametric,
The transition probability matrix for semi-Markov multi-state models fitted to time-to-event data with flexsurvreg. This has r,s entry giving the probability that an individual is in state s at time t, given they are in state r at time 0.
providing 
.