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.
|Author||Bob Rudis [aut, cre] (<https://orcid.org/0000-0001-5670-2640>), 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 <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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