Simulate Omics-Scale Data with Dependency R package dependentsimr version 1.0.0.0 Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and 'DESeq2' (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) <doi:10.1101/2025.01.31.634335>. Software Depends: R (>= 4.2) Imports: rlang (>= 1.0.0) Suggests: DESeq2 (>= 1.40.0), S4Vectors (>= 0.44.0), SummarizedExperiment (>= 1.36.0), MASS (>= 7.3), corpcor (>= 1.6.0), testthat (>= 3.0.0), Matrix (>= 1.7), sparsesvd (>= 0.2), knitr (>= 1.50), rmarkdown, BiocManager, remotes, tidyverse (>= 2.0.0) Thomas Brooks <tgbrooks@gmail.com> Comprehensive R Archive Network (CRAN) Thomas Brooks [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-6980-0079>) MIT + file LICENSE (https://CRAN.R-project.org/package=dependentsimr/LICENSE) 2025-07-23 application/tgz https://CRAN.R-project.org/package=dependentsimr doi:10.32614/CRAN.package.dependentsimr