variable-selection

Tutorial on model assessment, model selection and inference after model selection

Projection predictive variable selection

Estimating Copula Entropy (Mutual Information) and Transfer Entropy (Conditional Independence) 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/

Data preparation for data science projects.

R package for estimating copula entropy (mutual information) and transfer entropy (conditional independence)

Stability Selection with Error Control

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

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.

Awesome papers on Feature Selection

Code and simulations using Biologically Annotated Neural Networks (BANNs)

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

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).

sliced: scikit-learn compatible sufficient dimension reduction

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

A regularized version of RBM for unsupervised feature selection.

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

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.

A statistical framework for feature selection and association mapping with 3D shapes

Automated Bidirectional Stepwise Selection On Python

Solution for the precisionFDA Brain Cancer Predictive Modeling Challenge using msaenet

Methods for selecting diverse (molecular) database.