This repository contains the source code for the paper "DERANDOMIZED TRUNCATED D-VINE COPULA KNOCKOFFS WITH E-VALUES TO CONTROL THE FALSE DISCOVERY RATE."
The computational implementations of the proposed methods were carried out using R and Python programming languages, employing versions 3.6.3 and 3.9.7, respectively. The code for running the simulations and the application to a real dataset uses several packages from R and Python. The R packages are the following: dplyr (version 1.0.9), glmnet (version 4.1.4), rvinecopulib (version 0.6.2.1.3), knockoff (version 0.3.5), sn(version 2.0.2), doMC (version 1.3.8), foreach (version 1.5.2), doParallel (version 1.0.17), TSP (version 1.2.4), and VineCopula (version 2.4.5). The Python libraries are the following: numpy (version 1.20.3), pandas (version 1.3.4), joblib (version 1.1.0), matplotlib (version 3.4.3), knockpy (version 1.1.1), and rpy2 (version 3.5.1). The folder "Simulations" contains code related to 4 four distinct data-generating processes (DGPs) for the predictors X considered in the paper: a truncated D-vine copula, a multivariate normal distribution, a t-tailed Markov chain, and a parametric D-vine copula fitted to gene expression data. Each configuration has two Jupyter notebook files, one for Gaussian Linear response and the other for Logistic regression response. The folder "Application" contains code and data for applying the proposed methodology to a real lung cancer dataset.