White Noise Normalization for Mass Spectrometry Profiling Data R package winn version 0.1.5 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. Software Depends: R (>= 3.5.0) Imports: stats, lmtest, mgcv, splines Suggests: knitr, rmarkdown, sva, testthat (>= 3.0.0) Tanmay Tanna <tanmay@tanmaytanna.com> Comprehensive R Archive Network (CRAN) Tanmay Tanna [aut, cre, cph] GPL-3 2026-09-27 application/tgz https://CRAN.R-project.org/package=winn doi:10.32614/CRAN.package.winn