high-dimensional-statistics

[ICML2026] The first, fully verified, sorry-free, large-scale Lean 4 library for statistical learning theory, covering infrastructures for mordern statistics and learning theory.

An R package for nonparametric covariance matrix estimation in high dimensions

High-dimensional statistics with R

R research code for debiased causal mediation in ultra-high-dimensional settings with interaction effects.

SpokeDarts sphere-packing sampling in any dimension. Advancing front sampling from radial lines (spokes) through prior samples.

Code Repo for "Regularized estimation of high-dimensional FAVAR models", JMLR, 2020

High Dimensional Portfolio Selection with Cardinality Constraints

Leveraging the full dimensionality of single-cell transcriptomics (among other things!)

Julia package to perform Bayesian clustering of high-dimensional Euclidean data using pairwise dissimilarity information.

Implementation of High-dimensional vector autoregression time series modeling via tensor decomposition, Di Wang, Yao Zheng, Heng Lian, Guodong Li. Written in JAX.

List of software packages for repeated measures analysis in -omics data

Bayesian survival models for high-dimensional data

Thresholded Sparse CCA for Multiple Measurements

Python scripts from paper Optimal cleaning for singular values of cross-covariance matrices, by Florent Benaych-Georges, Jean-Philippe Bouchaud, Marc Potters (see https://arxiv.org/abs/1901.05543)

Simulation for "Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics"

TRexSelector - Fast high-dimensional variable selection with FDR control in C++, Python and R.

Presentation of the VSC model, a variant of VAE - 📔 High Dimensional Statistics

Generalized Decomposition Priors on R2

Code Repo for "Penalized MLE of multi-layer Gaussian Graphical Models", JMLR, 2016

Code for the paper "Interpolation can hurt robust generalization even when there is no noise" available here: https://papers.nips.cc/paper/2021/hash/c4f2c88e16a579900657c18726641c81-Abstract.html