metadeconfoundR

cranv1.0.5

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 de

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TillBirkner/metadeconfoundR
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detectseparationlmtestforeachparalleldoParallelstatsloggerlme4ggplot2reshape2methodsrlangcirclizedplyrggraphigraphmagrittrscalesstringr
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First published · 2026-02-04 15:27:14

Last updated · 2026-02-04T13:40:02+00:00