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

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

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

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

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.

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.

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).

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

Stability Selection with Error Control

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).

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