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msm (version 1.5)

Multi-state Markov and Hidden Markov Models in Continuous Time

Description

Continuous-time Markov and hidden Markov multi-state models for longitudinal data. Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.

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Version

Install

install.packages('msm')

Monthly Downloads

18,399

Version

1.5

License

GPL (>= 2)

Maintainer

Christopher Jackson

Last Published

January 6th, 2015

Functions in msm (1.5)

Bootstrap resampling for multi-state models

paramdata.object

Developer documentation: internal msm parameters object

Misclassification probability matrix

msm.form.qoutput

Extract msm model parameter estimates in compact format

The delta method

Developer documentation: hidden Markov model structure object

Calculate crude initial values for transition intensities

Kaplan Meier estimates of incidence

Calculate tables of hazard ratios for covariates on transition intensities

Measurement error distributions

Developer documentation: model for covariates on transition intensities

plot.survfit.msm

Plot empirical and fitted survival curves

Score residuals

Mean sojourn times from a multi-state model

Extract model log-likelihood

Parameters of phase-type models in mixture form

Transient and absorbing states

Hidden Markov model constructors

Truncated Normal distribution

Expected first passage time

Criteria for comparing two multi-state models with nested state spaces

Developer documentation: transition model structure object

Convert data for `msm' to data for `survival', `mstate' or `flexsurv' analysis

Developer documentation: misclassification model structure object

Tables of observed and expected prevalences

Aortic aneurysm progression data

plot.prevalence.msm

Plot of observed and expected prevalences

Total length of stay, or expected number of visits

Summarise a fitted multi-state model

Explore the likelihood surface

Simulate from a Markov model fitted using msm

Estimated ratio of transition intensities

Matrix exponential

Bronchiolitis obliterans syndrome after lung transplants

Calculate tables of odds ratios for covariates on misclassification probabilities

Simulate multiple trajectories from a multi-state Markov model with arbitrary observation times

Print a fitted msm model object

Developer documentation: censoring model object

Plots of multi-state models

Probability of each state being next

model.frame.msm

Extract original data from msm objects.

Transition probability matrix

Generic function to find quantiles of a distribution

Exponential distribution with piecewise-constant rate

Pearson-type goodness-of-fit test

Likelihood ratio test

pmatrix.piecewise.msm

Transition probability matrix for processes with piecewise-constant intensities

Print a fitted msm model object

Extract model coefficients

Passage probabilities

Table of transitions

recreate.olddata

Convert data stored in msm object to old format

Psoriatic arthritis data

Transition intensity matrix

FEV1 measurements from lung transplant recipients

Fitted msm model objects

Multi-state Markov and hidden Markov models in continuous time

Calculate the probabilities of underlying states and the most likely path through them

Coxian phase-type distribution with two phases

Heart transplant monitoring data

Simulate one individual trajectory from a continuous-time Markov model

Developer documentation: model for covariates on misclassification probabilities