ggplot2, graphics, methods, rmarkdown, scales, stats, shiny
(>= 1.5.0), utils
shinytest, testthat (>= 3.0.0), vdiffr (>= 1.0.0)

The twoStepsBenchmark() and threeRuleSmooth() functions allow you to
disaggregate a low-frequency time-serie with higher frequency time-series,
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).
graphics, grDevices, methods, RColorBrewer (>= 1.1-2), stats,
utils
'bflSmooth.R' 'data.R' 'disaggR.R' 'in.R' 's4register.R'
'twoStepsBenchmark.R' 'methods.R' 'plot.R' 'praislm.R'
'reView.R' 'threeRuleSmooth.R' 'utils.R'

c(person(given = "Arnaud",
family = "Feldmann",
role = "aut",
email = "arnaud.feldmann@gmail.com",
comment = c(ORCID = "0000-0003-0109-7505","Author, creator and maintener of the package until the version 1.0.2")),
person(given = "Franck",
family = "Arnaud",
role = "ctb",
comment = "barplot base graphics method for the mts class"),
person(given = "Thomas",
family = "Laurent",
role = "cre",
email = "thomas.laurent@insee.fr"),
person(given = "Institut national de la statistique et des études économiques",
role = "cph",
email = "comptes-trimestriels@insee.fr",
comment = "https://www.insee.fr/"))
Arnaud Feldmann [aut] (<https://orcid.org/0000-0003-0109-7505>, Author,
creator and maintener of the package until the version 1.0.2),
Franck Arnaud [ctb] (barplot base graphics method for the mts class),
Thomas Laurent [cre],
Institut national de la statistique et des études économiques [cph]
(https://www.insee.fr/)

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>).
'bflSmooth.R' 'data.R' 'disaggR.R' 'utils.R' 'in.R'
's4register.R' 'twoStepsBenchmark.R' 'methods.R' 'plot.R'
'praislm.R' 'reView.R' 'threeRuleSmooth.R'