Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors

A Python toolbox for gaining geometric insights into high-dimensional data

Vald. A Highly Scalable Distributed Vector Search Engine

Fast Best-Subset Selection Library

A collection of small-sample, high-dimensional microarray data sets to assess machine-learning algorithms and models.

High-dimensional medians (medoid, geometric median, etc.). Fast implementations in Python.

A Framework for Dimensionality Reduction in R

Poisson pseudo-likelihood regression with multiple levels of fixed effects

Deep distance-based outlier detection published in KDD18: Learning representations specifically for distance-based outlier detection. Few-shot outlier detection

A Toolkit for Interactive Statistical Data Visualization

Implementation of NEWMA: a new method for scalable model-free online change-point detection

A Python package for hubness analysis and high-dimensional data mining

🔮 Benchmarking and visualization toolkit for penalized Cox models

Statistical quality evaluation of dimensionality reduction algorithms

The DPA package is the scikit-learn compatible implementation of the Density Peaks Advanced clustering algorithm. The algorithm provides robust and visual information about the clusters, their statistical reliability and their hierarchical organization.

A general purpose Snakemake workflow and MrBiomics module to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.

An interactive 3D web viewer of up to million points on one screen that represent data. Provides interaction for viewing high-dimensional data that has been previously embedded in 3D or 2D. Based on graphosaurus.js and three.js. For a Linux release of a complete embedding+visualization pipeline please visit https://github.com/sonjageorgievska/Em…

A fast high dimensional near neighbor search algorithm based on group testing and locality sensitive hashing

CorBinian: A toolbox for modelling and simulating high-dimensional binary and count-data with correlations