{
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  "Package": "smmR",
  "Title": "Simulation, Estimation and Reliability of Semi-Markov Models",
  "Version": "1.0.4",
  "Date": "2023-02-03",
  "Authors@R": "c(person(\"Vlad Stefan\", \"Barbu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-0840-016X\")),\nperson(\"Florian\", \"Lecocq\", role = c(\"aut\")),\nperson(\"Corentin\", \"Lothode\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-8209-317X\")),\nperson(\"Nicolas\", \"Vergne\", role = c(\"aut\", \"cre\"),\nemail = \"nicolas.vergne@univ-rouen.fr\"))",
  "Description": "Performs parametric and non-parametric estimation and\nsimulation for multi-state discrete-time semi-Markov processes.\nFor the parametric estimation, several discrete distributions\nare considered for the sojourn times: Uniform, Geometric,\nPoisson, Discrete Weibull and Negative Binomial. The\nnon-parametric estimation concerns the sojourn time\ndistributions, where no assumptions are done on the shape of\ndistributions. Moreover, the estimation can be done on the\nbasis of one or several sample paths, with or without censoring\nat the beginning or/and at the end of the sample paths.\nReliability indicators such as reliability, maintainability,\navailability, BMP-failure rate, RG-failure rate, mean time to\nfailure and mean time to repair are available as well. The\nimplemented methods are described in Barbu, V.S., Limnios, N.\n(2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios,\nN. (2008) <doi:10.1080/10485250701261913> and Trevezas, S.,\nLimnios, N. (2011) <doi:10.1080/10485252.2011.555543>.\nEstimation and simulation of discrete-time k-th order Markov\nchains are also considered.",
  "URL": "https://plmlab.math.cnrs.fr/lmrs/statistique/smmR,\nhttps://lmrs.pages.math.cnrs.fr/statistique/smmR",
  "BugReports": "https://github.com/corentin-dev/smmR/issues",
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  "Repository": "https://corentin-dev.r-universe.dev",
  "Date/Publication": "2023-04-05 08:30:16 UTC",
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  "Author": "Vlad Stefan Barbu [aut] (ORCID:\n<https://orcid.org/0000-0002-0840-016X>),\nFlorian Lecocq [aut],\nCorentin Lothode [aut] (ORCID: <https://orcid.org/0000-0002-8209-317X>),\nNicolas Vergne [aut, cre]",
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    "failureRate",
    "fitmm",
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    "get.f",
    "get.H",
    "get.Kpar",
    "get.limitDistribution",
    "get.Niuj",
    "get.P",
    "get.psi",
    "get.Py",
    "get.qy",
    "get.stationaryDistribution",
    "getKernel",
    "getProcesses",
    "is.mm",
    "is.mmfit",
    "is.smm",
    "is.smmfit",
    "is.smmnonparametric",
    "is.smmparametric",
    "maintainability",
    "matrixConvolution",
    "meanRecurrenceTimes",
    "meanSojournTimes",
    "mm",
    "mttf",
    "mttr",
    "reliability",
    "setSeed",
    "smmnonparametric",
    "smmparametric"
  ],
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    {
      "page": "smmR-package",
      "title": "smmR : Semi-Markov Models, Markov Models and Reliability",
      "topics": [
        "smmR-package",
        "smmR"
      ]
    },
    {
      "page": "availability",
      "title": "Availability Function",
      "topics": [
        "availability"
      ]
    },
    {
      "page": "convolution",
      "title": "Discrete-time convolution product of f and g (See definition 2.2 p. 20)",
      "topics": [
        "convolution"
      ]
    },
    {
      "page": "failureRate",
      "title": "Failure Rate Function",
      "topics": [
        "failureRate"
      ]
    },
    {
      "page": "fitmm",
      "title": "Maximum Likelihood Estimation (MLE) of a k-th order Markov chain",
      "topics": [
        "fitmm"
      ]
    },
    {
      "page": "fitsmm",
      "title": "Maximum Likelihood Estimation (MLE) of a semi-Markov chain",
      "topics": [
        "fitsmm"
      ]
    },
    {
      "page": "get.f",
      "title": "Method to get the conditional sojourn time distribution f",
      "topics": [
        "get.f"
      ]
    },
    {
      "page": "get.H",
      "title": "Function to compute the value of the sojourn time cumulative distribution H",
      "topics": [
        "get.H"
      ]
    },
    {
      "page": "get.Kpar",
      "title": "Method to get the number of parameters of a Markov or semi-Markov chain",
      "topics": [
        "get.Kpar"
      ]
    },
    {
      "page": "get.limitDistribution",
      "title": "Method to get the limit (stationary) distribution",
      "topics": [
        "get.limitDistribution"
      ]
    },
    {
      "page": "get.Niuj",
      "title": "Function giving the value of the counting process Niuj used in the estimation of the kernel and the transition matrix of censored and non-parametric semi-markov chains (cf. article Exact MLE and asymptotic properties for nonparametric semi-Markov models)",
      "topics": [
        "get.Niuj"
      ]
    },
    {
      "page": "get.P",
      "title": "Method to compute the value of P",
      "topics": [
        "get.P"
      ]
    },
    {
      "page": "get.psi",
      "title": "Function to compute the value of the matrix-valued function psi",
      "topics": [
        "get.psi"
      ]
    },
    {
      "page": "get.Py",
      "title": "Method to compute the value of P_{Y}",
      "topics": [
        "get.Py"
      ]
    },
    {
      "page": "get.qy",
      "title": "Method to get the semi-Markov kernel q_{Y}",
      "topics": [
        "get.qy"
      ]
    },
    {
      "page": "get.stationaryDistribution",
      "title": "Method to get the stationary distribution",
      "topics": [
        "get.stationaryDistribution"
      ]
    },
    {
      "page": "getKernel",
      "title": "Method to get the semi-Markov kernel q",
      "topics": [
        "getKernel"
      ]
    },
    {
      "page": "getProcesses",
      "title": "Function to compute processes based on a list of sequences",
      "topics": [
        "getProcesses"
      ]
    },
    {
      "page": "is.mm",
      "title": "Function to check if an object is of class 'mm'",
      "topics": [
        "is.mm"
      ]
    },
    {
      "page": "is.mmfit",
      "title": "Function to check if an object is of class 'mmfit'",
      "topics": [
        "is.mmfit"
      ]
    },
    {
      "page": "is.smm",
      "title": "Function to check if an object is of class 'smm'",
      "topics": [
        "is.smm"
      ]
    },
    {
      "page": "is.smmfit",
      "title": "Function to check if an object is of class 'smmfit'",
      "topics": [
        "is.smmfit"
      ]
    },
    {
      "page": "is.smmnonparametric",
      "title": "Function to check if an object is of class 'smmnonparametric'",
      "topics": [
        "is.smmnonparametric"
      ]
    },
    {
      "page": "is.smmparametric",
      "title": "Function to check if an object is of class 'smmparametric'",
      "topics": [
        "is.smmparametric"
      ]
    },
    {
      "page": "maintainability",
      "title": "Maintainability Function",
      "topics": [
        "maintainability"
      ]
    },
    {
      "page": "matrixConvolution",
      "title": "Discrete-time matrix convolution product (See definition 3.5 p. 48)",
      "topics": [
        "matrixConvolution"
      ]
    },
    {
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      "title": "Method to get the mean recurrence times mu",
      "topics": [
        "meanRecurrenceTimes"
      ]
    },
    {
      "page": "meanSojournTimes",
      "title": "Mean Sojourn Times Function",
      "topics": [
        "meanSojournTimes"
      ]
    },
    {
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      "title": "Markov model specification",
      "topics": [
        "mm"
      ]
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    {
      "page": "mttf",
      "title": "Mean Time To Failure (MTTF) Function",
      "topics": [
        "mttf"
      ]
    },
    {
      "page": "mttr",
      "title": "Mean Time To Repair (MTTR) Function",
      "topics": [
        "mttr"
      ]
    },
    {
      "page": "plot.smm",
      "title": "Plot function for an object of class smm",
      "topics": [
        "plot.smm"
      ]
    },
    {
      "page": "plot.smmfit",
      "title": "Plot function for an object of class smmfit",
      "topics": [
        "plot.smmfit"
      ]
    },
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      "title": "Reliability Function",
      "topics": [
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      ]
    },
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      "title": "Set the RNG Seed from within Rcpp",
      "topics": [
        "setSeed"
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      "title": "Simulates Markov chains",
      "topics": [
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      "topics": [
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      ]
    },
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