tdigest
Wicked Fast, Accurate Quantiles Using t-Digests
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.4.3 |
rolling linux/jammy R-4.5 | tdigest_0.4.3.tar.gz |
44.0 KiB |
0.4.3 |
rolling linux/noble R-4.5 | tdigest_0.4.3.tar.gz |
44.1 KiB |
0.4.3 |
rolling source/ R- | tdigest_0.4.3.tar.gz |
15.6 KiB |
0.4.3 |
latest linux/jammy R-4.5 | tdigest_0.4.3.tar.gz |
44.0 KiB |
0.4.3 |
latest linux/noble R-4.5 | tdigest_0.4.3.tar.gz |
44.1 KiB |
0.4.3 |
latest source/ R- | tdigest_0.4.3.tar.gz |
15.6 KiB |
0.4.3 |
2026-04-23 source/ R- | tdigest_0.4.3.tar.gz |
0 B |
0.4.2 |
2025-04-20 source/ R- | tdigest_0.4.2.tar.gz |
15.6 KiB |