Provides a decision-guided workflow for correcting technical variability in chemical profiling data. Tests for white noise identify measured features that need correction while preserving those that already pass. The workflow combines robust outlier adjustment, adaptive drift detection, change-point segmentation, batch correction, and probabilistic quotient normalization in a single pipeline or as modular steps. It supports parameter tuning using pooled quality-control samples as well as operation for studies without pooled controls.
| Version: | 0.1.5 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, lmtest, mgcv, splines |
| Suggests: | knitr, rmarkdown, sva, testthat (≥ 3.0.0) |
| Published: | 2026-09-27 |
| DOI: | 10.32614/CRAN.package.winn (may not be active yet) |
| Author: | Tanmay Tanna [aut, cre, cph] |
| Maintainer: | Tanmay Tanna <tanmay at tanmaytanna.com> |
| BugReports: | https://github.com/ratschlab/winn/issues |
| License: | GPL-3 |
| URL: | https://github.com/ratschlab/winn |
| NeedsCompilation: | no |
| Citation: | winn citation info |
| Materials: | README, NEWS |
| CRAN checks: | winn results |
| Reference manual: | winn.html , winn.pdf |
| Vignettes: |
WiNN Tutorial: A Reproducible LC-MS Example (source, R code) |
| Package source: | winn_0.1.5.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): winn_0.1.5.tgz, r-oldrel (arm64): winn_0.1.5.tgz, r-release (x86_64): winn_0.1.5.tgz, r-oldrel (x86_64): winn_0.1.5.tgz |
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