R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
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Updated
Jun 7, 2024 - R
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
Variable Selection with Knockoffs
Predict and prevent customer churn in the telecom industry with this project. Leverage advanced analytics and ML on a diverse dataset to build a robust classification model. Gain a deep understanding of customer behavior and identify key factors influencing churn. Clone the repository, explore insights, and enhance customer retention startegies.
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
Variable Selection Network with PyTorch
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
The goal of the project is to predict Life Expectancy using various factors and to determine the relationship that exists between them.
🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions
R package for fitting semiparametric accelerated failure time models in high dimensions
Package for analyzing GWAS summary statistics data
Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).
Projection predictive variable selection
📈 Ordered Homogeneity Pursuit Lasso for Group Variable Selection
A novel meta-analysis framework of the R-squared (R2)-based mediation effect estimation for high-dimensional omics mediators
Methods for selecting diverse (molecular) database.
BAS R package https://merliseclyde.github.io/BAS/
Efficient Variable Selection for GLMs in R
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.
Linear Model Interaction Terms Optimizer
Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.
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