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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.
Version: | 0.4.2 |
Depends: | R (≥ 3.5.0) |
Imports: | magrittr, stats |
Suggests: | testthat, covr, spelling |
Published: | 2024-06-19 |
DOI: | 10.32614/CRAN.package.tdigest |
Author: | Bob Rudis |
Maintainer: | Bob Rudis <bob at rud.is> |
BugReports: | https://todo.sr.ht/~hrbrmstr/tdigest |
License: | MIT + file LICENSE |
Copyright: | file inst/COPYRIGHTS tdigest copyright details |
URL: | https://git.sr.ht/~hrbrmstr/tdigest |
NeedsCompilation: | yes |
Language: | en-US |
CRAN checks: | tdigest results |
Reference manual: | tdigest.pdf |
Package source: | tdigest_0.4.2.tar.gz |
Windows binaries: | r-devel: tdigest_0.4.2.zip, r-release: tdigest_0.4.2.zip, r-oldrel: tdigest_0.4.2.zip |
macOS binaries: | r-release (arm64): tdigest_0.4.2.tgz, r-oldrel (arm64): tdigest_0.4.2.tgz, r-release (x86_64): tdigest_0.4.2.tgz, r-oldrel (x86_64): tdigest_0.4.2.tgz |
Old sources: | tdigest archive |
Reverse depends: | meboot |
Reverse imports: | PAMscapes |
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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.