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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.
Documentation:
Downloads:
Reverse dependencies:
Linking:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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[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>)