Package: disaggR 1.0.5.3
Pauline Meinzel
disaggR: Two-Steps Benchmarks for Time Series Disaggregation
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>).
Authors:
disaggR_1.0.5.3.tar.gz
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disaggR_1.0.5.3.tgz(r-4.4-any)disaggR_1.0.5.3.tgz(r-4.3-any)
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disaggR.pdf |disaggR.html✨
disaggR/json (API)
NEWS
| # Install 'disaggR' in R: |
| install.packages('disaggR', repos = c('https://inseefr.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/inseefr/disaggr/issues
disaggregationstatistical-packagetime-series
Last updated 3 months agofrom:c2a531babc. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 12 2024 |
| R-4.5-win | OK | Sep 12 2024 |
| R-4.5-linux | OK | Sep 12 2024 |
| R-4.4-win | OK | Sep 12 2024 |
| R-4.4-mac | OK | Sep 12 2024 |
| R-4.3-win | OK | Sep 12 2024 |
| R-4.3-mac | OK | Sep 12 2024 |
Exports:annualBenchmarkbflSmoothdefault_col_paldefault_lty_paldefault_marginsdefault_theme_ggplotdistancein_disaggrin_revisionsin_samplein_scatterMath2model.listOpsoutlierspraisrePortresiduals_extrapreUseBenchmarkreViewrhoseshowsmoothed.partsmoothed.ratethreeRuleSmoothtwoStepsBenchmark
Dependencies:RColorBrewer
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Smooth a time series | bflSmooth |
| Distance computation for disaggregations | distance |
| Comparing a disaggregation with the high-frequency input | in_disaggr |
| Comparing two disaggregations together | in_revisions |
| Producing the in sample predictions of a prais-lm regression | in_sample |
| Comparing the inputs of a praislm regression | in_scatter |
| Plotting disaggR objects | autoplot.threeRuleSmooth autoplot.tscomparison autoplot.twoStepsBenchmark plot.threeRuleSmooth plot.tscomparison plot.twoStepsBenchmark |
| Producing a report | rePort |
| Using an estimated benchmark model on another time series | reUseBenchmark |
| A shiny app to reView and modify twoStepsBenchmarks | reView |
| Bends a time series with a lower frequency one by smoothing their rate | threeRuleSmooth |
| Regress and bends a time series with a lower frequency one | annualBenchmark twoStepsBenchmark |
