Authors@R: c(person("Alexander", "Seipp", role ="cre", email = "alexander.seipp@uni-oldenburg.de"),
Description: Semiparametric distributional regression methods (expectile, quantile and mode regression) for time-to-event variables with right-censoring; uses inverse probability of censoring weights or accelerated failure time models with auxiliary likelihoods. Expectile regression using inverse probability of censoring weights has been introduced in Seipp et al. (2021) "Weighted Expectile Regression for Right-Censored Data" <doi:10.1002/sim.9137>, mode regression for time-to-event variables has been introduced in Seipp et al. (2022) "Flexible Semiparametric Mode Regression for Time-to-Event Data" (accepted manuscript).

Title: Two-Steps Benchmarks for Time Series Disaggregation
person(given = "Institut national de la statistique et des études économiques",
Description: The twoStepsBenchmark() function and its wrappers 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 is either quarterly or mensual.
See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8).

comment = "barplot base graphics method for the mts class"),
Description: 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
Imports: ggplot2, rmarkdown, scales, shiny (>= 1.5.0)
Suggests: shinytest, testthat (>= 2.1.0), vdiffr
Collate: 'bflSmooth.R' 'data.R' 'disaggR.R' 'in.R' 's4declaration.R'
Author: Arnaud Feldmann [aut, cre] (<https://orcid.org/0000-0003-0109-7505>),
Franck Arnaud [ctb] (barplot base graphics method for the mts class),