metadeconfoundR
Covariate-Sensitive Analysis of Cross-Sectional High-Dimensional Data
v1.0.5
·
Feb 4, 2026
·
GPL-2
Description
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.
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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
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1.0.5
Mar 10, 2026