Methods for Checking the Markov Condition in Multi-State
Survival Data
c(
person(
given = "Gustavo",
family = "Soutinho",
role = c("aut", "cre"),
email = "gustavosoutinho@sapo.pt",
comment = c(ORCID = "0000-0002-0559-1327")
),
person(
given = "Luis",
family = "Meira-Machado",
role = "aut",
email = "lmachado@math.uminho.pt",
comment = c(ORCID = "0000-0002-8577-7665")
)
)
The inference in multi-state models is traditionally performed under
a Markov assumption that claims that past and future of the process
are independent given the present state. In this package, we
consider tests of the Markov assumption that are applicable to
general multi-state models. Three approaches using existing
methodology are considered: a simple method based on including
covariates depending on the history in Cox models for the transition
intensities; methods based on measuring the discrepancy of the
non-Markov estimators of the transition probabilities to the
Markov Aalen-Johansen estimators; and, finally, methods that were
developed by considering summaries from families of log-rank
statistics where patients are grouped by the state occupied of the
process at a particular time point (see Soutinho G, Meira-Machado L
(2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H
(2020) <doi:10.1093/biostatistics/kxaa030>).
Gustavo Soutinho [aut, cre] (<https://orcid.org/0000-0002-0559-1327>),
Luis Meira-Machado [aut] (<https://orcid.org/0000-0002-8577-7665>)

Package to perform discrete Markov chain analysis
Package to easily handle discrete Markov chains
R (>= 2.14), methods, expm, matlab, igraph

A package to handle and analyze discrete Markov chains

A package to handle and analyse discrete Markov chains
A package for easily handling discrete Markov chains

igraph, Matrix, matlab, expm, stats4, parallel

Easy Handling Discrete Time Markov Chains
The package contains classes and method to create and manage (plot, print, export for example) discrete time Markov chains (DTMC). In addition it provide functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of DTMC proprieties) analysis.