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

A Framework for Dimensionality Reduction in R

🔮 Benchmarking and visualization toolkit for penalized Cox models

Sparse and Regularized Discriminant Analysis in R

🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions

An R package for testing high-dimensional covariance matrices

A R package for multi-dimensional data visualization

locus R package - Large-scale variational inference for variable selection in sparse multiple-response regression

R package to implement high-dimensional confounding adjustment using continuous spike and slab priors

Bayesian Logistic Regression with Hyper-LASSO priors

📈 Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Repository for the paper: "Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death" by Gisby et al. doi: https://doi.org/10.7554/eLife.64827

Robust Sure Independence Screening using the Minimum Density Power Divergence Estimators

Multivariate Outlierdetection In Contingency Tables

Code for a paper on estimation and evaluation of penalized survival models with high dimensional left-truncated and right-censored (LTRC) survival data

High dimensional shrinkage optimal portfolios in R

High-dimensional change point detection in Gaussian Graphical models with missing values

Использование методов машинного обучения для прогнозирования инвестиций в России

Generalized Linear Models with the Exclusive Lasso Penalty