tdigest: Wicked Fast, Accurate Quantiles Using t-Digests

The t-Digest construction algorithm, by Dunning et al., (2019) <doi:10.48550/arXiv.1902.04023>, uses a variant of 1-dimensional k-means clustering to produce a very compact data structure that allows accurate estimation of quantiles. This t-Digest data structure can be used to estimate quantiles, compute other rank statistics or even to estimate related measures like trimmed means. The advantage of the t-Digest over previous digests for this purpose is that the t-Digest handles data with full floating point resolution. The accuracy of quantile estimates produced by t-Digests can be orders of magnitude more accurate than those produced by previous digest algorithms. Methods are provided to create and update t-Digests and retrieve quantiles from the accumulated distributions.

Version: 0.4.2
Depends: R (≥ 3.5.0)
Imports: magrittr, stats
Suggests: testthat, covr, spelling
Published: 2024-06-19
DOI: 10.32614/CRAN.package.tdigest
Author: Bob Rudis ORCID iD [aut, cre], Ted Dunning [aut] (t-Digest algorithm; <https://github.com/tdunning/t-digest/>), Andrew Werner [aut] (Original C+ code; <https://github.com/ajwerner/tdigest>)
Maintainer: Bob Rudis <bob at rud.is>
BugReports: https://todo.sr.ht/~hrbrmstr/tdigest
License: MIT + file LICENSE
Copyright: file inst/COPYRIGHTS
tdigest copyright details
URL: https://git.sr.ht/~hrbrmstr/tdigest
NeedsCompilation: yes
Language: en-US
CRAN checks: tdigest results

Documentation:

Reference manual: tdigest.pdf

Downloads:

Package source: tdigest_0.4.2.tar.gz
Windows binaries: r-devel: tdigest_0.4.2.zip, r-release: tdigest_0.4.1.zip, r-oldrel: tdigest_0.4.2.zip
macOS binaries: r-release (arm64): tdigest_0.4.2.tgz, r-oldrel (arm64): tdigest_0.4.2.tgz, r-release (x86_64): tdigest_0.4.2.tgz, r-oldrel (x86_64): tdigest_0.4.2.tgz
Old sources: tdigest archive

Reverse dependencies:

Reverse depends: meboot
Reverse imports: PAMscapes

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