Displaying 1 to 7 from 7 results

turf - A modular geospatial engine written in JavaScript

  •    Javascript

Turf is a JavaScript library for spatial analysis. It includes traditional spatial operations, helper functions for creating GeoJSON data, and data classification and statistics tools. Turf can be added to your website as a client-side plugin, or you can run Turf server-side with Node.js (see below).Download the minified file, and include it in a script tag. This will expose a global variable named turf.

tdigest - tdigest: javascript implementation of Dunning's T-Digest for streaming quantile approximation

  •    Javascript

The T-Digest is a data structure and algorithm for constructing an approximate distribution for a collection of real numbers presented as a stream. The algorithm makes no guarantees, but behaves well enough in practice that implementations have been included in Apache Mahout and ElasticSearch for computing summaries and approximate order statistics over a stream. For an overview of T-Digest's behavior, see Davidson-Pilon's blog post regarding a python implementation. For more details, there are the tdigest paper and reference implementation (Java). This javascript implementation is based on a reading of the paper, with some boundary and performance tweaks.

tdigest - An implementation of Ted Dunning's t-digest in Go.

  •    C++

This is an implementation of Ted Dunning's t-digest in Go. The implementaion is based off Derrick Burns' C++ implementation.

redis-tdigest - t-digest module for Redis

  •    C

This is a Redis module for the t-digest data structure which can be used for accurate online accumulation of rank-based statistics such as quantiles and cumulative distribution at a point. The implementation is based on the Merging Digest implementation by the author. Before going ahead, make sure that the Redis server you're using has support for Redis modules.




tdigest - t-Digest data structure in Python

  •    Python

This is a Python implementation of Ted Dunning's t-digest data structure. The t-digest data structure is designed around computing accurate estimates from either streaming data, or distributed data. These estimates are percentiles, quantiles, trimmed means, etc. Two t-digests can be added, making the data structure ideal for map-reduce settings, and can be serialized into much less than 10kB (instead of storing the entire list of data). tdigest is compatible with both Python 2 and Python 3.

stats - A C++ header-only library of statistical distribution functions.

  •    C++

StatsLib is a templated C++ library of statistical distribution functions, featuring unique compile-time computing capabilities and seamless integration with several popular linear algebra libraries. The following options should be declared before including the StatsLib header files.






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