metadeconfoundR
Covariate-Sensitive Analysis of Cross-Sectional High-Dimensional Data
Using non-parametric tests, naive associations between omics features and metadata in cross-sectional data-sets are detected. In a second step, confounding effects between metadata associated to the same omics feature are detected and labeled using nested post-hoc model comparison tests, as first described in Forslund, Chakaroun, Zimmermann-Kogadeeva, et al. (2021) <doi:10.1038/s41586-021-04177-9>. The generated output can be graphically summarized using the built-in plotting function.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.5 |
rolling linux/jammy R-4.5 | metadeconfoundR_1.0.5.tar.gz |
876.8 KiB |
1.0.5 |
rolling linux/noble R-4.5 | metadeconfoundR_1.0.5.tar.gz |
876.7 KiB |
1.0.5 |
rolling source/ R- | metadeconfoundR_1.0.5.tar.gz |
1.4 MiB |
1.0.5 |
latest linux/jammy R-4.5 | metadeconfoundR_1.0.5.tar.gz |
876.8 KiB |
1.0.5 |
latest linux/noble R-4.5 | metadeconfoundR_1.0.5.tar.gz |
876.7 KiB |
1.0.5 |
latest source/ R- | metadeconfoundR_1.0.5.tar.gz |
1.4 MiB |
1.0.5 |
2026-04-26 source/ R- | metadeconfoundR_1.0.5.tar.gz |
1.4 MiB |
1.0.5 |
2026-04-23 source/ R- | metadeconfoundR_1.0.5.tar.gz |
1.4 MiB |
1.0.5 |
2026-04-09 windows/windows R-4.5 | metadeconfoundR_1.0.5.zip |
875.3 KiB |
1.0.2 |
2025-04-20 source/ R- | metadeconfoundR_1.0.2.tar.gz |
778.0 KiB |