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  "Title": "Simulation and Estimation of Multi-State Discrete-Time\nSemi-Markov and Markov Models",
  "Version": "1.0.2",
  "Date": "2020-01-24",
  "Author": "Vlad Stefan Barbu, Caroline Berard, Dominique Cellier, Mathilde\nSautreuil and Nicolas Vergne",
  "Maintainer": "Nicolas Vergne <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. 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.",
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  "VignetteBuilder": "utils",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-03 07:37:33 UTC",
    "User": "root"
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  "Date/Publication": "2020-01-31 12:30:02 UTC",
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  "_stars": 1,
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    "extra/citation.html",
    "extra/citation.json",
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    "extra/contents.json",
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      "date": "2017-09-08"
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    "AIC_SM",
    "BIC_Mk",
    "BIC_SM",
    "estimMk",
    "estimSM",
    "InitialLawMk",
    "InitialLawSM",
    "LoglikelihoodMk",
    "LoglikelihoodSM",
    "simulMk",
    "simulSM"
  ],
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    {
      "page": "SMM-package",
      "title": "SMM : Semi-Markov and Markov Models",
      "topics": [
        "SMM-package",
        "SMM"
      ]
    },
    {
      "page": "AIC_Mk",
      "title": "AIC (Markov model)",
      "topics": [
        "AIC_Mk"
      ]
    },
    {
      "page": "AIC_SM",
      "title": "AIC (semi-Markov model)",
      "topics": [
        "AIC_SM"
      ]
    },
    {
      "page": "BIC_Mk",
      "title": "BIC (Markov model)",
      "topics": [
        "BIC_Mk"
      ]
    },
    {
      "page": "BIC_SM",
      "title": "BIC (semi-Markov model)",
      "topics": [
        "BIC_SM"
      ]
    },
    {
      "page": "estimMk",
      "title": "Estimation of a k-th order Markov chain",
      "topics": [
        "estimMk"
      ]
    },
    {
      "page": "estimSM",
      "title": "Estimation of a semi-Markov chain",
      "topics": [
        "estimSM"
      ]
    },
    {
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      "title": "Estimation of the initial law (Markov model)",
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        "InitialLawMk"
      ]
    },
    {
      "page": "InitialLawSM",
      "title": "Estimation of the initial law (semi-Markov model)",
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        "InitialLawSM"
      ]
    },
    {
      "page": "LoglikelihoodMk",
      "title": "Loglikelihood (Markov model)",
      "topics": [
        "LoglikelihoodMk"
      ]
    },
    {
      "page": "LoglikelihoodSM",
      "title": "Loglikelihood (semi-Markov model)",
      "topics": [
        "LoglikelihoodSM"
      ]
    },
    {
      "page": "simulMk",
      "title": "Simulation of a k-th order Markov chain",
      "topics": [
        "simulMk"
      ]
    },
    {
      "page": "simulSM",
      "title": "Simulation of a semi-Markov chain",
      "topics": [
        "simulSM"
      ]
    }
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      "title": "SMM Vignette",
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