An R package for assumption-lean covariance matrix estimation in high dimensions
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Updated
Feb 17, 2024 - R
An R package for assumption-lean covariance matrix estimation in high dimensions
High-dimensional statistics with R
SpokeDarts sphere-packing sampling in any dimension. Advancing front sampling from radial lines (spokes) through prior samples.
Regularized estimation of high-dimensional FAVAR models
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
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"
Official code repository for "Penalized MLE of multi-layer Gaussian Graphical Models"
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
Bayesian survival models for high-dimensional data
Fast and flexible models for extremal events.
High-dimensional Statistics in R, online workshop 27 Feb - 1 Mar
Covariate-varying Networks
High-dimensional Statistics in R, online workshop 19 - 22 Mar
Computational Statistics (Summer Semester 2022)
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