Package: tdigest 0.4.2

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.

Authors:Bob Rudis [aut, cre], Ted Dunning [aut], Andrew Werner [aut]

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Peer review:

Bug tracker:https://github.com/hrbrmstr/tdigest/issues

On CRAN:

quantilet-digest

11 exports 37 stars 3.07 score 1 dependencies 4 dependents 10 scripts 594 downloads

Last updated 3 months agofrom:e52b746007. Checks:OK: 8 NOTE: 1. Indexed: yes.

Exports:%>%as_tdigestis_tdigesttd_addtd_createtd_mergetd_quantile_oftd_total_counttd_value_attdigesttquantile

Dependencies:magrittr