https://paulinemeinzel.r-universe.devRecent package updates in paulinemeinzelR-universehttps://github.com/paulinemeinzel.pnghttps://paulinemeinzel.r-universe.devMon, 13 Jul 2026 20:29:59 GMTpauline.meinzel@insee.fr (Pauline Meinzel)The twoStepsBenchmark() and threeRuleSmooth() functions allow you to disaggregate a low-frequency time series with higher frequency time series, using the French National Accounts methodology. The aggregated sum of the resulting time series is strictly equal to the low-frequency time series within the benchmarking window. Typically, the low-frequency time series is an annual one, unknown for the last year, and the high frequency one is either quarterly or monthly. See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8, <https://www.insee.fr/en/information/2579410>).https://github.com/r-universe/inseefr/actions/runs/38130051603Mon, 13 Jul 2026 20:29:59 GMTdisaggR1.0.6.999successhttps://inseefr.r-universe.devhttps://github.com/inseefr/disaggrdisaggr.Rmddisaggr.htmlIntroduction to disaggR2021-08-16 19:18:112026-07-13 18:20:55disaggr_outlier.Rmddisaggr_outlier.htmlOutliers in two-step benchmarks2022-07-19 08:23:332026-07-13 18:20:55