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 [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://gitlab.com/hrbrmstr/tdigest/issues
License:	MIT + file LICENSE
Copyright:	file inst/COPYRIGHTS tdigest copyright details
URL:	https://gitlab.com/hrbrmstr/tdigest
NeedsCompilation:	yes
Language:	en-US
Materials:	NEWS
CRAN checks:	tdigest results

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