msm: Multi-state Markov and hidden Markov models in continuous time

Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation schemes are supported, including processes observed at arbitrary times (panel data), continuously-observed processes, and censored states. 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.

Version: 1.4
Depends: R (≥ 2.10)
Imports: survival, mvtnorm, expm
Suggests: mstate, minqa, doParallel, numDeriv, testthat
Published: 2014-07-08
Author: Christopher Jackson
Maintainer: Christopher Jackson <chris.jackson at mrc-bsu.cam.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: msm citation info
Materials: NEWS ChangeLog
In views: Distributions, Survival
CRAN checks: msm results

Downloads:

Reference manual: msm.pdf
Vignettes: User guide to msm with worked examples
Package source: msm_1.4.tar.gz
Windows binaries: r-devel: msm_1.4.zip, r-release: msm_1.4.zip, r-oldrel: msm_1.4.zip
OS X Snow Leopard binaries: r-release: msm_1.4.tgz, r-oldrel: msm_1.4.tgz
OS X Mavericks binaries: r-release: msm_1.4.tgz
Old sources: msm archive

Reverse dependencies:

Reverse depends: ATmet, BaSTA, BVS, eiPack, hysteresis, lordif, ltm, NEff, NHMM, parfm, RM2, rriskDistributions, spatial.gev.bma, Surrogate, trioGxE
Reverse imports: Biograph, breakpoint, CIDnetworks, clustMD, gems, iBATCGH, optBiomarker, phytools, Rchoice, RMark, rtdists
Reverse suggests: flexsurv, geiger, oro.pet, surveillance