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msm (version 1.6.8)

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

15,206

Version

1.6.8

License

GPL (>= 2)

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Repository

https://github.com/chjackson/msm

Maintainer

Christopher Jackson

Last Published

December 16th, 2019

Functions in msm (1.6.8)

Bootstrap resampling for multi-state models

Developer documentation: misclassification model structure object

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

Multivariate hidden Markov models

Misclassification probability matrix

Developer documentation: model for covariates on misclassification probabilities

Developer documentation: hidden Markov model structure object

Expected first passage time

FEV1 measurements from lung transplant recipients

Calculate tables of hazard ratios for covariates on transition intensities

Hidden Markov model constructors

Calculate tables of odds ratios for covariates on misclassification probabilities

paramdata.object

Developer documentation: internal msm parameters object

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

Fitted msm model objects

msm.form.qoutput

Extract msm model parameter estimates in compact format

Multi-state Markov and hidden Markov models in continuous time

Likelihood ratio test

Extract model log-likelihood

Measurement error distributions

model.frame.msm

Extract original data from msm objects.

Kaplan Meier estimates of incidence

Exponential distribution with piecewise-constant rate

Pearson-type goodness-of-fit test

Transition probability matrix

Parameters of phase-type models in mixture form

Plots of multi-state models

Developer documentation: transition model structure object

Estimated ratio of transition intensities

Print a fitted msm model object

Print a fitted msm model object

Developer documentation: model for covariates on transition intensities

plot.survfit.msm

Plot empirical and fitted survival curves

Psoriatic arthritis data

plot.prevalence.msm

Plot of observed and expected prevalences

pmatrix.piecewise.msm

Transition probability matrix for processes with piecewise-constant intensities

Probability of each state being next

Transition intensity matrix

Generic function to find quantiles of a distribution

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

Explore the likelihood surface

Truncated Normal distribution

Mean sojourn times from a multi-state model

recreate.olddata

Convert data stored in msm object to old format

Passage probabilities

Score residuals

Tables of observed and expected prevalences

Simulate from a Markov model fitted using msm

Simulate one individual trajectory from a continuous-time Markov model

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

Table of transitions

Total length of stay, or expected number of visits

Summarise a fitted multi-state model

Transient and absorbing states

Calculate crude initial values for transition intensities

Coxian phase-type distribution with two phases

Bronchiolitis obliterans syndrome after lung transplants

Aortic aneurysm progression data

Matrix exponential

The delta method

Heart transplant monitoring data

Developer documentation: censoring model object

Extract model coefficients