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deseats

1.1.2

Data-Driven Locally Weighted Regression for Trend and Seasonality in TS

0packages depend
5.3Kdownloads / year
10.7%test coverage
13/13checks pass

Overview

About
Maintained by Dominik SchulzFirst published 2023-11-084 releasesCRAN page ↗

Various methods for the identification of trend and seasonal components in time series (TS) are provided. Among them is a data-driven locally weighted regression approach with automatically selected bandwidth for equidistant short-memory time series. The approach is a combination / extension of the algorithms by Feng (2013) doi:10.1080/02664763.2012.740626 and Feng, Y., Gries, T., and Fritz, M. (2020) doi:10.1080/10485252.2020.1759598 and a brief description of this new method is provided in the package documentation. Furthermore, the package allows its users to apply the base model of the Berlin procedure, version 4.1, as described in Speth (2004) https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile. Permission to include this procedure was kindly provided by the Federal Statistical Office of Germany.

Install

Health

CRAN checks
13OK
Slowest check: 5.1 min · r-devel-windows-x86_64
Code health
Yes
Tests · ratio 0.05
10.7%
Coverage · measured lines
100%
Documentation · exports
17
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-05-02
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-04-22
    12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-04-18
    11 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Show 1 earlier snapshots
  • NOTE2026-03-10
    12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 386 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo
Examples that run
56%
Documented parameters
96%
Return-value docs
100%
References docs
8%

Downloads

5.3K
CRAN downloads in the past year
Rank #5,929 · ~15/day · ~445/mo
Daily download trend is not available in this view yet.
23830 days
1.4K90 days
5.3K1 year
Compare downloads with other packages →
Also on439 r2u17 autocran27 c2d4u

Dependencies

Declared dependencies
19 external dependencies (excludes base and recommended)
Depends (2)
R >= 2.10methods
LinkingTo (2)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (2)
Maintainer (1)
Author, Maintainer
Authors (2)
Author, Maintainer
Package Timeline

4 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.1.2Latest
    2026-03-16 · current release · diff ↗
  • 1.1.1
    2025-06-23 · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • 1.1.0
    2024-07-12 · diff ↗
  • R
    R 4.4.0 released · 2024-04-24
  • 1.0.0
    2023-11-08
  • R
    R 4.3.0 released · 2023-04-21

Package metadata

First published
2023-11-08
Total releases
4 / 3 yrs
License
GPL-3 OSI
Minimum R
≥ 2.10
Bundled data
28 KB / 15 files
Download size
311 KB
Installed size
not tracked yet
With dependencies
not tracked yet
Appears in task views

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("deseats")
Schulz, D., & Feng, Y. (2026). deseats: Data-Driven Locally Weighted Regression for Trend and Seasonality in TS (Version 1.1.2) [Computer software]. https://doi.org/10.32614/CRAN.package.deseats

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Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for deseats version 1.1.2 [Data set]. HJJB, LLC. Data release v2026-08-18. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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