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 + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | dependentsimr results |
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