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msm (version 1.6.9)

Multi-State Markov and Hidden Markov Models in Continuous Time

Description

Functions for fitting continuous-time Markov and hidden Markov multi-state models to 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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Install

install.packages('msm')

Monthly Downloads

19,259

Version

1.6.9

License

GPL (>= 2)

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Repository

https://github.com/chjackson/msm

Maintainer

Christopher Jackson

Last Published

September 27th, 2021

Functions in msm (1.6.9)

Heart transplant monitoring data

The delta method

Bronchiolitis obliterans syndrome after lung transplants

Aortic aneurysm progression data

Matrix exponential

Calculate crude initial values for transition intensities

Bootstrap resampling for multi-state models

Developer documentation: censoring model object

Coxian phase-type distribution with two phases

Extract model coefficients

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

Calculate tables of hazard ratios for covariates on transition intensities

Expected first passage time

Multivariate hidden Markov models

Developer documentation: hidden Markov model structure object

Misclassification probability matrix

Hidden Markov model constructors

Developer documentation: model for covariates on misclassification probabilities

FEV1 measurements from lung transplant recipients

Developer documentation: misclassification model structure object

Measurement error distributions

Fitted msm model objects

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

paramdata.object

Developer documentation: internal msm parameters object

Likelihood ratio test

Calculate tables of odds ratios for covariates on misclassification probabilities

Extract model log-likelihood

msm.form.qoutput

Extract msm model parameter estimates in compact format

Multi-state Markov and hidden Markov models in continuous time

model.frame.msm

Extract original data from msm objects.

Plots of multi-state models

Parameters of phase-type models in mixture form

Pearson-type goodness-of-fit test

Exponential distribution with piecewise-constant rate

Kaplan Meier estimates of incidence

Psoriatic arthritis data

Transition probability matrix

plot.prevalence.msm

Plot of observed and expected prevalences

plot.survfit.msm

Plot empirical and fitted survival curves

Explore the likelihood surface

Print a fitted msm model object

Print a fitted msm model object

Developer documentation: transition model structure object

pmatrix.piecewise.msm

Transition probability matrix for processes with piecewise-constant intensities

Estimated ratio of transition intensities

Passage probabilities

Probability of each state being next

Truncated Normal distribution

Generic function to find quantiles of a distribution

Tables of observed and expected prevalences

Transition intensity matrix

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

Mean sojourn times from a multi-state model

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

recreate.olddata

Convert data stored in msm object to old format

Score residuals

Table of transitions

Summarise a fitted multi-state model

Developer documentation: model for covariates on transition intensities

Simulate from a Markov model fitted using msm

Simulate one individual trajectory from a continuous-time Markov model

Transient and absorbing states

Total length of stay, or expected number of visits