quantile-estimation

Skream is a high-performance time-series library with memory-footprint guarantees.

Probabilistic Data Structures and Algorithms in Python

DynaHist: A Dynamic Histogram Library for Java

[Mach. Learn. 2026] Official implementation of "Monotone Composite Quantile Regression via Second-Order Gradient Boosting Framework"

C++ version of Ted Dunning's merging t-digest

Distributional Gradient Boosting Machines

An extension of Py-Boost to probabilistic modelling

Wicked Fast, Accurate Quantiles Using 'T-Digests'

A library to compute histograms on distributed environments, on streaming data

Monotone Composite Quantile Regression Neural Network (MCQRNN) implemented in both TensorFlow and PyTorch

Agnostic (re)implementations (R/SAS/Python/C) of common quantile estimation algorithms.

Monotone Composite Quantile Generalized Additive Model

[JCGS 2021] Official Implement of "Learning Multiple Quantiles With Neural Networks"

Prometheus summary with quantiles

C++14 port of the DDSketch distributed quantile sketch algorithm

Sorting Composite Quantile Regression Neural Network (SCQRNN) implemented in PyTorch

Python Implementation of Graham Cormode and S. Muthukrishnan's Effective Computation of Biased Quantiles over Data Streams in ICDE’05

B-digest is a Go library for fast and memory-efficient estimation of quantiles with guaranteed relative error and full mergeability

This repository is the official implementation of `Impute Missing Entries with Uncertainty' (AAAI, 2026).

Compute least squares estimates and IVX estimates with pairwise quantile predictive regressions (R package)