There are 4 repositories under variable-selection topic.
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
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
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 https://merliseclyde.github.io/BAS/
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
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.
Awesome papers on Feature Selection
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
Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.
Code for Variable Selection in Black Box Methods with RelATive cEntrality (RATE) Measures
Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics
Best Subset Selection algorithm for Regression, Classification, Count, Survival analysis
Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).
A regularized version of RBM for unsupervised feature selection.
🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions
Variable Selection Network with PyTorch
Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)
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.
Automated Bidirectional Stepwise Selection On Python
A statistical framework for feature selection and association mapping with 3D shapes
Robust Sure Independence Screening using the Minimum Density Power Divergence Estimators
Knockoff-based analysis of GWAS summary statistics data
Variable Selection with Knockoffs