tdigest: Wicked Fast, Accurate Quantiles Using t-Digests

The t-Digest construction algorithm, by Dunning et al., (2019) <arXiv:1902.04023v1>, 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: 0.3.0
Depends: R (≥ 3.5.0)
Imports: magrittr, stats
Suggests: testthat, covr, spelling
Published: 2019-08-01
Author: Bob Rudis ORCID iD [aut, cre], Ted Dunning [aut] (t-Digest algorithm; <>), Andrew Werner [aut] (Original C+ code; <>)
Maintainer: Bob Rudis <bob at>
License: MIT + file LICENSE
Copyright: file inst/COPYRIGHTS
tdigest copyright details
NeedsCompilation: yes
Language: en-US
Materials: NEWS
CRAN checks: tdigest results


Reference manual: tdigest.pdf
Package source: tdigest_0.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: tdigest_0.3.0.tgz, r-oldrel: tdigest_0.3.0.tgz

Reverse dependencies:

Reverse depends: meboot
Reverse imports: NNS


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