Modern use of slope estimation often involves the (repeated) estimation of a large number of slopes on a large number of data points. Some of the most popular non-parametric and robust alternatives to the least squares estimator are the Theil-Sen and Siegel’s repeated median slope estimators. The robslopes package contains fast algorithms for these slope estimators. The implemented randomized algorithms run in \(\mathcal{O}(n\log(n))\) and \(\mathcal{O}(n\log^2(n))\) expected time respectively and use \(\mathcal{O}(n)\) space. They achieve speedups up to a factor \(10^3\) compared with existing implementations for common sample sizes, as illustrated in a benchmark study, and they allow for the possibility of estimating the slopes on samples of size \(10^5\) and larger thanks to the limited space usage. Finally, the original algorithms are adjusted in order to properly handle duplicate values in the data set.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-012.zip
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For attribution, please cite this work as
Raymaekers, "The R Journal: robslopes: Efficient Computation of the (Repeated) Median Slope", The R Journal, 2023
BibTeX citation
@article{RJ-2023-012,
author = {Raymaekers, Jakob},
title = {The R Journal: robslopes: Efficient Computation of the (Repeated) Median Slope},
journal = {The R Journal},
year = {2023},
note = {https://doi.org/10.32614/RJ-2023-012},
doi = {10.32614/RJ-2023-012},
volume = {14},
issue = {4},
issn = {2073-4859},
pages = {38-49}
}