msm: Multi-State Markov and Hidden Markov Models in Continuous Time
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.
Version:
1.8.2
Published:
2024年11月07日
Author:
Christopher Jackson [aut, cre]
Maintainer:
Christopher Jackson <chris.jackson at mrc-bsu.cam.ac.uk>
NeedsCompilation:
yes
Documentation:
Downloads:
Reverse dependencies:
Reverse imports:
ammiBayes,
ARCensReg,
ARDL,
ASSET,
ASSIGN,
BayesianTools,
BayesRGMM,
biodosetools,
bsam,
BSTZINB,
ccostr,
CensSpatial,
cfda,
clustMD,
CopCTS,
cosa,
decisionSupport,
DFIT,
difNLR,
drugdevelopR,
dsp,
eatRep,
ei,
eiPack,
EMC2,
epiphy,
EventPredInCure,
extrafrail,
Families,
FLORAL,
frailtyEM,
gems,
gmnl,
grmsem,
hesim,
hmsr,
horseshoenlm,
httk,
ICcalib,
icmstate,
interactionR,
interactionRCS,
kardl,
methInheritSim,
OneSampleMR,
optBiomarker,
oro.pet,
PanCanVarSel,
parfm,
Petersen,
pexm,
Rchoice,
riAFTBART,
RMark,
rriskDistributions,
rtdists,
scDECO,
semmcmc,
SightabilityModel,
simExam,
SimKid,
sparsereg,
SPAS,
spBFA,
spCP,
stochLAB,
surrosurv,
tradepolicy,
TrenchR,
TropFishR,
varTestnlme,
VirtualPop,
womblR,
wxgenR
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