Wicked Fast, Accurate Quantiles Using t-Digests

The t-Digest construction algorithm, by Dunning et al., (2019) , 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.


Reference manual

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0.3.0 by Bob Rudis, a year ago


Report a bug at https://gitlab.com/hrbrmstr/tdigest/issues

Browse source code at https://github.com/cran/tdigest

Authors: 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>)

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports magrittr, stats

Suggests testthat, covr, spelling

Imported by NNS.

Depended on by meboot.

See at CRAN