Easy Handling Discrete Time Markov Chains

Source: R/markovchain.R

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

Details

Package:markovchain
Type:Package
Version:0.8.2
Date:2020-01-5
License:GPL-2
Depends:R (>= 3.6.0), methods, expm, matlab, igraph, Matrix

References

Discrete-Time Markov Models, Bremaud, Springer 1999

Author

Giorgio Alfredo Spedicato Maintainer: Giorgio Alfredo Spedicato <spedicato_giorgio@yahoo.it>

Examples

# create some markov chains
statesNames=c("a","b")
mcA<-new("markovchain", transitionMatrix=matrix(c(0.7,0.3,0.1,0.9),byrow=TRUE,
         nrow=2, dimnames=list(statesNames,statesNames)))
         
statesNames=c("a","b","c")
mcB<-new("markovchain", states=statesNames, transitionMatrix=
         matrix(c(0.2,0.5,0.3,0,1,0,0.1,0.8,0.1), nrow=3, 
         byrow=TRUE, dimnames=list(statesNames, statesNames)))

statesNames=c("a","b","c","d")
matrice<-matrix(c(0.25,0.75,0,0,0.4,0.6,0,0,0,0,0.1,0.9,0,0,0.7,0.3), nrow=4, byrow=TRUE)
mcC<-new("markovchain", states=statesNames, transitionMatrix=matrice)
mcD<-new("markovchain", transitionMatrix=matrix(c(0,1,0,1), nrow=2,byrow=TRUE))


#operations with S4 methods
mcA^2

#> Unnamed Markov chain^2 
#>  A  2 - dimensional discrete Markov Chain defined by the following states: 
#>  a, b 
#>  The transition matrix  (by rows)  is defined as follows: 
#>      a    b
#> a 0.52 0.48
#> b 0.16 0.84
#> 
steadyStates(mcB)

#>      a b c
#> [1,] 0 1 0
absorbingStates(mcB)

#> [1] "b"
markovchainSequence(n=20, markovchain=mcC, include=TRUE)

#>  [1] "a" "a" "b" "b" "b" "b" "b" "b" "a" "a" "a" "b" "b" "b" "b" "b" "b" "b" "b"
#> [20] "a" "b"