{ "_id": "6ad9cf65992ad85420b6002f", "Package": "MisRepARMA", "Type": "Package", "Title": "Misreported Time Series Analysis", "Version": "0.2.0", "Date": "2026-06-07", "Encoding": "UTF-8", "Authors@R": "c(\nperson(given = \"David\",\nfamily = \"Moriña Soler\",\nrole = c(\"aut\", \"cre\"),\nemail = \"David.Morina@uab.cat\",\ncomment = c(ORCID = \"0000-0001-5949-7443\")),\nperson(given = \"Amanda\",\nfamily = \"Fernández-Fontelo\",\nrole = \"aut\",\nemail = \"Amanda.Fernandez@uab.cat\"),\nperson(given = \"Alejandra\",\nfamily = \"Cabaña\",\nrole = \"aut\",\nemail = \"AnaAlejandra.Cabana@uab.cat\"),\nperson(given = \"Pedro\",\nfamily = \"Puig\",\nrole = \"aut\",\nemail = \"Pere.Puig@uab.cat\"),\nperson(given = \"Biel\",\nfamily = \"Abarca Galván\",\nrole = \"aut\"))", "Maintainer": "David Moriña Soler ", "Description": "Provides a simple and trustworthy methodology for the\nanalysis of misreported continuous time series using either a\nfrequentist (bootstrap-based EM algorithm) or a Bayesian (MCMC\nvia JAGS) approach. The frequentist method is described in\nMorina et al. (2021) . The\nBayesian extension fits the same ARMA model with misreporting\nstructure using a full posterior distribution, providing\ncredible intervals and DIC for model comparison, as described\nin Morina et al. 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