dependentsimr: Simulate Omics-Scale Data with Dependency

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>.

Version:	1.0.0.0
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)
Published:	2025-07-23
DOI:	10.32614/CRAN.package.dependentsimr
Author:	Thomas Brooks [aut, cre, cph]
Maintainer:	Thomas Brooks <tgbrooks at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	dependentsimr results

Documentation:

Reference manual:	dependentsimr.html , dependentsimr.pdf
Vignettes:	simulate_data (source, R code)

Downloads:

Package source:	dependentsimr_1.0.0.0.tar.gz
Windows binaries:	r-devel: dependentsimr_1.0.0.0.zip, r-release: dependentsimr_1.0.0.0.zip, r-oldrel: dependentsimr_1.0.0.0.zip
macOS binaries:	r-release (arm64): dependentsimr_1.0.0.0.tgz, r-oldrel (arm64): dependentsimr_1.0.0.0.tgz, r-release (x86_64): dependentsimr_1.0.0.0.tgz, r-oldrel (x86_64): dependentsimr_1.0.0.0.tgz

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