{ "_id": "6a267c3b794753ddbc3ef066", "Package": "disaggR", "Type": "Package", "Title": "Two-Steps Benchmarks for Time Series Disaggregation", "Version": "1.0.5.4", "Authors@R": "c(person(given = \"Arnaud\",\nfamily = \"Feldmann\",\nrole = \"aut\",\nemail = \"arnaud.feldmann@gmail.com\",\ncomment = c(ORCID = \"0000-0003-0109-7505\",\"Author and maintener of the package until the version 1.0.1\")),\nperson(given = \"Pauline\",\nfamily = \"Meinzel\",\nrole = \"cre\",\nemail = \"pauline.meinzel@insee.fr\"),\nperson(given = \"Thomas\",\nfamily = \"Laurent\",\nrole = \"ctb\",\ncomment = \"Maintener of the package from 1.0.2 to 1.0.5.2\"),\nperson(given = \"Franck\",\nfamily = \"Arnaud\",\nrole = \"ctb\",\ncomment = \"barplot base graphics method for the mts class\"),\nperson(given = \"Institut national de la statistique et des études économiques\",\nrole = \"cph\",\nemail = \"comptes-trimestriels@insee.fr\",\ncomment = \"https://www.insee.fr/\"))", "Description": "The twoStepsBenchmark() and threeRuleSmooth() functions\nallow you to disaggregate a low-frequency time series with\nhigher frequency time series, using the French National\nAccounts methodology. The aggregated sum of the resulting time\nseries is strictly equal to the low-frequency time series\nwithin the benchmarking window. Typically, the low-frequency\ntime series is an annual one, unknown for the last year, and\nthe high frequency one is either quarterly or monthly. 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