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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:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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