Case studies on model assessment, model selection and inference after model selection

Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python

No description provided.

Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models

Projection predictive variable selection

Boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data. The current relase version can be found on CRAN (http://cran.r-project.org/package=mboost).

BAS R package for Bayesian Model Averaging and Variable Selection

R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test

Awesome papers on Feature Selection

OmicSelector - Environment, docker-based application and R package for biomarker signiture selection (feature selection) & deep learning diagnostic tool development from high-throughput high-throughput omics experiments and other multidimensional datasets. Initially developed for miRNA-seq, RNA-seq and qPCR.

Python library for selecting diverse data subsets for machine-learning. Webserver: https://huggingface.co/spaces/QCDevs/Selector.

Data preparation for data science projects.

Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.

Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm

Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).

Stability Selection with Error Control

No description provided.

Code and simulations using biologically annotated neural networks

Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).

sliced: scikit-learn compatible sufficient dimension reduction

Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics

Code for Variable Selection in Black Box Methods with RelATive cEntrality (RATE) Measures

Best Subset Selection algorithm for Regression, Classification, Count, Survival analysis

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

A regularized version of RBM for unsupervised feature selection.

Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)

Variable Selection Network with PyTorch

This repository commits to the application of biostatistics knowledge on clinical, randomized trials and observational studies.

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

l1l2py is a Python package to perform variable selection by means of l1l2 regularization with double optimization.