Simulation, Estimation and Reliability of Semi-Markov Models
Availability Function
Discrete-time convolution product of and (See definition 2.2 p....
BMP-Failure Rate Function
Failure Rate Function
Maximum Likelihood Estimation (MLE) of a k-th order Markov chain
Maximum Likelihood Estimation (MLE) of a semi-Markov chain
Method to get the conditional sojourn time distribution f
Function to compute the value of the sojourn time cumulative distribut...
Method to get the number of parameters of a Markov or semi-Markov chai...
Method to get the limit (stationary) distribution
Function giving the value of the counting process Niuj used in the est...
Method to compute the value of
Function to compute the value of the matrix-valued function
Method to compute the value of
Method to get the semi-Markov kernel
Method to get the stationary distribution
Method to get the semi-Markov kernel
Function to compute processes based on a list of sequences
Function to check if an object is of class mm
Function to check if an object is of class mmfit
Function to check if an object is of class smm
Function to check if an object is of class smmfit
Function to check if an object is of class smmnonparametric
Function to check if an object is of class smmparametric
Maintainability Function
Discrete-time matrix convolution product (See definition 3.5 p. 48)
Method to get the mean recurrence times
Mean Sojourn Times Function
Markov model specification
Mean Time To Failure (MTTF) Function
Mean Time To Repair (MTTR) Function
Plot function for an object of class smm
Plot function for an object of class smmfit
Reliability Function
Set the RNG Seed from within Rcpp
Simulates k-th order Markov chains
Simulates Markov chains
Simulates semi-Markov chains
Simulates semi-Markov chains
Non-parametric semi-Markov model specification
Parametric semi-Markov model specification
smmR : Semi-Markov Models, Markov Models and Reliability
Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. Reliability indicators such as reliability, maintainability, availability, BMP-failure rate, RG-failure rate, mean time to failure and mean time to repair are available as well. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. Estimation and simulation of discrete-time k-th order Markov chains are also considered.
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