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tdigest (version 0.4.2)

Wicked Fast, Accurate Quantiles Using t-Digests

Description

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.

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Install

install.packages('tdigest')

Monthly Downloads

551

Version

0.4.2

License

MIT + file LICENSE

Maintainer

Last Published

June 19th, 2024

Functions in tdigest (0.4.2)

tquantile

Calculate sample quantiles from a t-Digest
%>%

Pipe operator
as.list.tdigest

Serialize a tdigest object to an R list or unserialize a serialized tdigest list back into a tdigest object
td_total_count

Total items contained in the t-Digest
td_merge

Merge one t-Digest into another
td_quantile_of

Return the quantile of the value
td_create

Allocate a new histogram
td_add

Add a value to the t-Digest with the specified count
td_value_at

Return the value at the specified quantile
tdigest

Create a new t-Digest histogram from a vector