Wicked Fast, Accurate Quantiles Using t-Digests R package tdigest version 0.4.3 The t-Digest construction algorithm, by Dunning, (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. Software Depends: R (>= 3.5.0) Imports: magrittr, stats Suggests: testthat, covr, spelling Bob Rudis <bob@rud.is> Comprehensive R Archive Network (CRAN) Bob Rudis [aut, cre] (ORCID: <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>) MIT + file LICENSE (https://CRAN.R-project.org/package=tdigest/LICENSE) 2026-05-05 application/tgz https://CRAN.R-project.org/package=tdigest doi:10.32614/CRAN.package.tdigest en-US