Package index • msm

Summarising data
`statetable.msm()`	Table of transitions
Fitting models
`msm()`	Multi-state Markov and hidden Markov models in continuous time
`crudeinits.msm()`	Calculate crude initial values for transition intensities
`hmmCat()` `hmmIdent()` `hmmUnif()` `hmmNorm()` `hmmLNorm()` `hmmExp()` `hmmGamma()` `hmmWeibull()` `hmmPois()` `hmmBinom()` `hmmBetaBinom()` `hmmNBinom()` `hmmBeta()` `hmmTNorm()` `hmmMETNorm()` `hmmMEUnif()` `hmmT()`	Hidden Markov model constructors
`hmmMV()`	Multivariate hidden Markov models
`msm2Surv()`	Convert data for `msm' to data for `survival', `mstate' or `flexsurv' analysis
Output from fitted models
`qmatrix.msm()`	Transition intensity matrix
`pmatrix.msm()`	Transition probability matrix
`pmatrix.piecewise.msm()`	Transition probability matrix for processes with piecewise-constant intensities
`sojourn.msm()`	Mean sojourn times from a multi-state model
`totlos.msm()` `envisits.msm()`	Total length of stay, or expected number of visits
`pnext.msm()`	Probability of each state being next
`ppass.msm()`	Passage probabilities
`efpt.msm()`	Expected first passage time
`qratio.msm()`	Estimated ratio of transition intensities
`hazard.msm()`	Calculate tables of hazard ratios for covariates on transition intensities
`coef(<msm>)`	Extract model coefficients
`boot.msm()`	Bootstrap resampling for multi-state models
`ematrix.msm()`	Misclassification probability matrix
`odds.msm()`	Calculate tables of odds ratios for covariates on misclassification probabilities
`viterbi.msm()`	Calculate the probabilities of underlying states and the most likely path through them
`phasemeans.msm()`	Parameters of phase-type models in mixture form
`plot(<msm>)`	Plots of multi-state models
`print(<msm>)` `printnew.msm()`	Print a fitted msm model object
`printold.msm()`	Print a fitted msm model object
`summary(<msm>)`	Summarise a fitted multi-state model
`msm.form.qoutput()` `msm.form.eoutput()`	Extract msm model parameter estimates in compact format
Tidy model outputs
`tidy(<msm>)`	Tidy the parameter estimates from an msm model
`tidy(<msm.est>)`	Tidy the output of pmatrix.msm and similar functions
`tidy(<msm.estbystate>)`	Tidy the output of totlos.msm and similar functions
`tidy(<msm.prevalence>)`	Tidy the output of prevalence.msm
Model checking and comparison
`prevalence.msm()`	Tables of observed and expected prevalences
`plot(<prevalence.msm>)`	Plot of observed and expected prevalences
`plot(<survfit.msm>)`	Plot empirical and fitted survival curves
`plotprog.msm()`	Kaplan Meier estimates of incidence
`logLik(<msm>)`	Extract model log-likelihood
`lrtest.msm()`	Likelihood ratio test
`pearson.msm()`	Pearson-type goodness-of-fit test
`draic.msm()` `drlcv.msm()`	Criteria for comparing two multi-state models with nested state spaces
`scoreresid.msm()`	Score residuals
`surface.msm()` `contour(<msm>)` `persp(<msm>)` `image(<msm>)`	Explore the likelihood surface
Simulation of data
`sim.msm()`	Simulate one individual trajectory from a continuous-time Markov model
`simmulti.msm()`	Simulate multiple trajectories from a multi-state Markov model with arbitrary observation times
`simfitted.msm()`	Simulate from a Markov model fitted using msm
Probability distributions
`dpexp()` `ppexp()` `qpexp()` `rpexp()`	Exponential distribution with piecewise-constant rate
`dtnorm()` `ptnorm()` `qtnorm()` `rtnorm()`	Truncated Normal distribution
`d2phase()` `p2phase()` `q2phase()` `r2phase()` `h2phase()`	Coxian phase-type distribution with two phases
`dmenorm()` `pmenorm()` `qmenorm()` `rmenorm()` `dmeunif()` `pmeunif()` `qmeunif()` `rmeunif()`	Measurement error distributions
`qgeneric()`	Generic function to find quantiles of a distribution
Datasets
`cav`	Heart transplant monitoring data
`psor`	Psoriatic arthritis data
`aneur`	Aortic aneurysm progression data
`bos` `bos3` `bos4`	Bronchiolitis obliterans syndrome after lung transplants
`fev`	FEV1 measurements from lung transplant recipients
Miscellaneous utilities
`deltamethod()`	The delta method
`MatrixExp()`	Matrix exponential
`model.frame(<msm>)` `model.matrix(<msm>)`	Extract original data from `msm` objects.
`recreate.olddata()`	Convert data stored in msm object to old format
`transient.msm()` `absorbing.msm()`	Transient and absorbing states
`hmodel2list()`	Convert a hmodel object to HMM constructor function calls
Package internals (advanced)
`msm.object`	Fitted msm model objects
`cmodel.object`	Developer documentation: censoring model object
`qmodel.object`	Developer documentation: transition model structure object
`qcmodel.object`	Developer documentation: model for covariates on transition intensities
`emodel.object`	Developer documentation: misclassification model structure object
`ecmodel.object`	Developer documentation: model for covariates on misclassification probabilities
`hmodel.object`	Developer documentation: hidden Markov model structure object
`paramdata.object`	Developer documentation: internal msm parameters object
`updatepars.msm()`	Update the maximum likelihood estimates in a fitted model object.
Package overview
`msm-package`	Multi-State Markov and Hidden Markov Models in Continuous Time

Reference

Summarising data

Fitting models

Output from fitted models

Tidy model outputs

Model checking and comparison

Simulation of data

Probability distributions

Datasets

Miscellaneous utilities

Package internals (advanced)

Package overview