disaggR: Two-Steps Benchmarks for Time Series Disaggregation

The twoStepsBenchmark() and threeRuleSmooth() functions allow you to disaggregate a low-frequency time-serie with time-series of higher frequency, using the French National Accounts methodology. The aggregated sum of the resulting time-serie is strictly equal to the low-frequency serie within the benchmarking window. Typically, the low-frequency serie is an annual one, unknown for the last year, and the high frequency one is either quarterly or mensual. See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8).

Version: 0.2.1
Depends: R (≥ 2.10), methods
Imports: ggplot2, rmarkdown, scales, shiny (≥ 1.5.0)
Suggests: shinytest, testthat (≥ 2.1.0), vdiffr
Published: 2021-05-03
Author: Arnaud Feldmann ORCID iD [aut, cre], Franck Arnaud [ctb] (barplot base graphics method for the mts class), Institut national de la statistique et des études économiques [cph] (https://www.insee.fr/)
Maintainer: Arnaud Feldmann <arnaud.feldmann at insee.fr>
BugReports: https://github.com/InseeFr/disaggR/issues
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
In views: TimeSeries
CRAN checks: disaggR results

Downloads:

Reference manual: disaggR.pdf
Package source: disaggR_0.2.1.tar.gz
Windows binaries: r-devel: disaggR_0.2.1.zip, r-release: disaggR_0.2.1.zip, r-oldrel: disaggR_0.2.1.zip
macOS binaries: r-release: disaggR_0.2.1.tgz, r-oldrel: disaggR_0.2.1.tgz
Old sources: disaggR archive

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