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Use Least Squares Polynomial Regression and Statistical Testing to Improve Savitzky-Golay

0packages depend
3.3Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) doi:10.1021/ac00113a006 for more information.

Install

Health

CRAN checks
13OK
Slowest check: 1.2 min · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-13
  • ERROR13 OK, 1 ERROR2026-03-12
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 0.0.1
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
2
Examples that run
100%2 run in a plain check
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
0%
Help pages citing references
50%

Downloads

3.3K
CRAN downloads in the past year
Rank #10,607 · ~9/day · ~276/mo
Daily download trend is not available in this view yet.
31830 days
96790 days
3.3K1 year
Compare downloads with other packages →
Also on109 r2u18 autocran138 c2d4u

Dependencies

Declared dependencies
0 external dependencies (excludes base and recommended)

Installing ADPF adds no packages beyond those that come with R.

Depends (3)
R >= 3.2.4statsutils
Imports (0)
none
LinkingTo (0)
none
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (2)
Maintainer since 0.0.1 (2017), Author
Releases (1)
  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • R
    R 3.6.0 released · 2019-04-26
  • R
    R 3.5.0 released · 2018-04-23
  • 0.0.1Latest
    2017-09-13 · current release
  • R
    R 3.4.0 released · 2017-04-21

Package metadata

First published
2017-09-13
Total releases
1 / 9 yrs
License
GPL-3 OSI
Minimum R
≥ 3.2.4
Bundled data
8.8 KB / 1 file
Download size
16 KB
Appears in task views

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("ADPF")
Kruse, S., & Barak, P. (2017). ADPF: Use Least Squares Polynomial Regression and Statistical Testing to Improve Savitzky-Golay (Version 0.0.1) [Computer software]. https://doi.org/10.32614/CRAN.package.ADPF

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

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 ADPF version 0.0.1 [Data set]. HJJB, LLC. Data release v2026-10-19. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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