{
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  "Package": "HMMRel",
  "Title": "Hidden Markov Models for Reliability and Maintenance",
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  "Authors@R": "c(\nperson(\"M.L.\",\"Gamiz\",\nemail=\"mgamiz@ugr.es\",\nrole = c(\"aut\", \"cre\", \"cph\")),\nperson(\"N.\",\"Limnios\",\nemail=\"nikolaos.limnios@utc.fr\",\nrole = c(\"aut\", \"cph\")),\nperson(\"M.C.\",\"Segovia-Garcia\",\nemail=\"msegovia@ugr.es\",\nrole = c(\"aut\", \"cph\"))\n)",
  "Author": "M.L. Gamiz [aut, cre, cph], N. Limnios [aut, cph], M.C.\nSegovia-Garcia [aut, cph]",
  "Maintainer": "M.L. Gamiz <mgamiz@ugr.es>",
  "Description": "Reliability Analysis and Maintenance Optimization using\nHidden Markov Models (HMM). The use of HMMs to model the state\nof a system which is not directly observable and instead\ncertain indicators (signals) of the true situation are provided\nvia a control system. A hidden model can provide key\ninformation about the system dependability, such as the\nreliability of the system and related measures. An estimation\nprocedure is implemented based on the Baum-Welch algorithm.\nClassical structures such as K-out-of-N systems and Shock\nmodels are illustrated. Finally, the maintenance of the system\nis considered in the HMM context and two functions for new\npreventive maintenance strategies are considered. Maintenance\nefficiency is measured in terms of expected cost. Methods are\ndescribed in Gamiz, Limnios, and Segovia-Garcia (2023)\n<doi:10.1016/j.ejor.2022.05.006>.",
  "License": "GPL-2",
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  "Packaged": {
    "Date": "2026-05-24 08:46:20 UTC",
    "User": "root"
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  "Repository": "https://mgamiz.r-universe.dev",
  "Date/Publication": "2024-11-19 14:20:06 UTC",
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      "title": "HMMRel - Hidden Markov Models for Reliability and Maintenance",
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        "HMMRel-package"
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