DynaHist: A Dynamic Histogram Library for Java
TypeScript implementation of the distributed quantile sketch algorithm DDSketch
C++14 port of the DDSketch distributed quantile sketch algorithm
Kernel density integral transformation: feature preprocessing and univariate clustering (TMLR, 2023)
R Package for Multivariate Ranks and Quantiles Using Optimal Transportation
High-integrity Python KLL sketch for streaming quantiles — mergeable, deterministic, zero-dependency, with optional C++17/SIMD acceleration.
Probabilistic demand forecasting for Ruby: Croston, SBA and TSB for intermittent demand, demand classification with automatic model selection, and calibrated quantiles for every period and the lead-time total. Pure Ruby, zero runtime dependencies.
TimesFM 2.5 fork that restores forecast_with_covariates and maps the quantile head to inventory policy.
A Node-Red node realizing the 𝗣² algorithm of Jain and Chlamtec (1985) for dynamic calculation of quantiles without storing observations
Statistics for Data Analysis | Quartiles, Quantiles and Interquartile Range
Python/Seaborn script drawing letter-value (boxen) plots of depth distributions, demonstrated on Mariana Trench bathymetry. Reusable scientific-plotting example.
Interactive probability-space axis and tick-rendering experiment for adaptive quantitative visualization.
Interactive R lab showing why quantiles cannot be averaged and how mergeable histograms preserve tail latency.
Benchmarking five approaches to quantiles over large datasets — DDSketch, t-digest, KLL, a fixed-width histogram sketch and an exact parallel-sort baseline. C++17 with CI on Linux and macOS, Python plotting.
Calculate quartiles, quantiles, and the inter-quartile range (IQR) for a variable
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