Learn R Programming

⚠️There's a newer version (1.8.1) of this package.Take me there.

msm (version 1.6.7)

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.

Copy Link

Version

Install

install.packages('msm')

Monthly Downloads

19,068

Version

1.6.7

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/chjackson/msm

Maintainer

Christopher Jackson

Last Published

March 18th, 2019

Functions in msm (1.6.7)

Expected first passage time

Developer documentation: model for covariates on transition intensities

Bootstrap resampling for multi-state models

Fitted msm model objects

Psoriatic arthritis data

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

Exponential distribution with piecewise-constant rate

Misclassification probability matrix

Kaplan Meier estimates of incidence

Pearson-type goodness-of-fit test

Calculate tables of hazard ratios for covariates on transition intensities

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

Multivariate hidden Markov models

Developer documentation: hidden Markov model structure object

Developer documentation: misclassification model structure object

FEV1 measurements from lung transplant recipients

Table of transitions

Extract model log-likelihood

Likelihood ratio test

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

plot.prevalence.msm

Plot of observed and expected prevalences

Truncated Normal distribution

Developer documentation: model for covariates on misclassification probabilities

Mean sojourn times from a multi-state model

Transition probability matrix

Calculate tables of odds ratios for covariates on misclassification probabilities

Simulate one individual trajectory from a continuous-time Markov model

Simulate from a Markov model fitted using msm

msm.form.qoutput

Extract msm model parameter estimates in compact format

plot.survfit.msm

Plot empirical and fitted survival curves

Developer documentation: transition model structure object

Explore the likelihood surface

Multi-state Markov and hidden Markov models in continuous time

paramdata.object

Developer documentation: internal msm parameters object

Hidden Markov model constructors

Passage probabilities

model.frame.msm

Extract original data from msm objects.

pmatrix.piecewise.msm

Transition probability matrix for processes with piecewise-constant intensities

Summarise a fitted multi-state model

Tables of observed and expected prevalences

Probability of each state being next

Estimated ratio of transition intensities

Total length of stay, or expected number of visits

Measurement error distributions

Transition intensity matrix

Parameters of phase-type models in mixture form

Print a fitted msm model object

Plots of multi-state models

Generic function to find quantiles of a distribution

recreate.olddata

Convert data stored in msm object to old format

Print a fitted msm model object

Score residuals

Transient and absorbing states

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

Extract model coefficients

Developer documentation: censoring model object

Calculate crude initial values for transition intensities

The delta method

Coxian phase-type distribution with two phases

Aortic aneurysm progression data

Matrix exponential

Bronchiolitis obliterans syndrome after lung transplants

Heart transplant monitoring data