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msm (version 1.7.1)

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

19,748

Version

1.7.1

License

GPL (>= 2)

Issues

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Repository

https://github.com/chjackson/msm

Homepage

https://chjackson.github.io/msm/

Maintainer

Christopher Jackson

Last Published

November 23rd, 2023

Functions in msm (1.7.1)

Developer documentation: model for covariates on misclassification probabilities

FEV1 measurements from lung transplant recipients

Developer documentation: hidden Markov model structure object

Misclassification probability matrix

Calculate tables of hazard ratios for covariates on transition intensities

Developer documentation: misclassification model structure object

Hidden Markov model constructors

Multivariate hidden Markov models

Extract model log-likelihood

Fitted msm model objects

model.frame.msm

Extract original data from msm objects.

Likelihood ratio test

Multi-state Markov and hidden Markov models in continuous time

Measurement error distributions

paramdata.object

Developer documentation: internal msm parameters object

Calculate tables of odds ratios for covariates on misclassification probabilities

Expected first passage time

msm.form.qoutput

Extract msm model parameter estimates in compact format

Multi-State Markov and Hidden Markov Models in Continuous Time

Pearson-type goodness-of-fit test

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

Parameters of phase-type models in mixture form

Exponential distribution with piecewise-constant rate

Generic function to find quantiles of a distribution

Plots of multi-state models

Developer documentation: transition model structure object

Transition intensity matrix

Estimated ratio of transition intensities

pmatrix.piecewise.msm

Transition probability matrix for processes with piecewise-constant intensities

Probability of each state being next

Transition probability matrix

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

Kaplan Meier estimates of incidence

recreate.olddata

Convert data stored in msm object to old format

Score residuals

plot.prevalence.msm

Plot of observed and expected prevalences

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

Coxian phase-type distribution with two phases

Passage probabilities

Tables of observed and expected prevalences

Developer documentation: model for covariates on transition intensities

Psoriatic arthritis data

Update the maximum likelihood estimates in a fitted model object.

Mean sojourn times from a multi-state model

plot.survfit.msm

Plot empirical and fitted survival curves

Print a fitted msm model object

Truncated Normal distribution

Print a fitted msm model object

Simulate one individual trajectory from a continuous-time Markov model

Explore the likelihood surface

Simulate from a Markov model fitted using msm

Total length of stay, or expected number of visits

Transient and absorbing states

Table of transitions

Summarise a fitted multi-state model

Developer documentation: censoring model object

Heart transplant monitoring data

Calculate crude initial values for transition intensities

Extract model coefficients

Matrix exponential

Bootstrap resampling for multi-state models

Aortic aneurysm progression data

Bronchiolitis obliterans syndrome after lung transplants

The delta method

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