tdigest
On-line accumulation of rank-based statistics
https://github.com/futurice/haskell-tdigest#readme
| Version on this page: | 0.2.1@rev:1 |
| LTS Haskell 24.28: | 0.3.1 |
| Stackage Nightly 2026-01-19: | 0.3.1 |
| Latest on Hackage: | 0.3.1 |
This version can be pinned in stack with:
tdigest-0.2.1@sha256:76459825c87529b1e6c43632b4dc3e3dbe34caf25bf2ab6f187d2f99a41038cb,3219Module documentation for 0.2.1
- Data
- Data.TDigest
- Data.TDigest.Internal
- Data.TDigest.NonEmpty
- Data.TDigest.Postprocess
- Data.TDigest.Postprocess.Internal
- Data.TDigest.Tree
- Data.TDigest.Tree.Internal
- Data.TDigest.Tree.NonEmpty
- Data.TDigest.Tree.Postprocess
- Data.TDigest.Vector
- Data.TDigest.Vector.Internal
- Data.TDigest.Vector.NonEmpty
- Data.TDigest.Vector.Postprocess
- Data.TDigest
Depends on 9 packages(full list with versions):
tdigest
A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means.
See original paper: “Computing extremely accurate quantiles using t-digest” by Ted Dunning and Otmar Ertl
Synopsis
λ *Data.TDigest > median (tdigest [1..1000] :: TDigest 3)
Just 499.0090729817737
Benchmarks
Using 50M exponentially distributed numbers:
- average: 16s; incorrect approximation of median, mostly to measure prng speed
- sorting using
vector-algorithms: 33s; using 1000MB of memory - sparking t-digest (using some
par): 53s - buffered t-digest: 68s
- sequential t-digest: 65s
Example histogram
tdigest-simple -m tdigest -d standard -s 100000 -c 10 -o output.svg -i 34
cp output.svg example.svg
inkscape --export-png=example.png --export-dpi=80 --export-background-opacity=0 --without-gui example.svg
