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

Projection predictive 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

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.

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).

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

Stability Selection with Error Control

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

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

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

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

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

Efficient Variable Selection for GLMs in R

Robust Sure Independence Screening using the Minimum Density Power Divergence Estimators

MOSS: Multi-Omic integration via Sparse Singular Decomposition

📈 Ordered Homogeneity Pursuit Lasso for Group Variable Selection

atlasqtl R package - Fast global-local hotspot QTL detection

Solution for the precisionFDA Brain Cancer Predictive Modeling Challenge using msaenet

A general variable selection approach in the presence of missing data in both covariates and outcomes. This approach exploits the flexibility of machine learning modeling techniques and bootstrap imputation, which is amenable to nonparametric methods in which the effect sizes of predictor variables are not naturally defined as in parametric mode…