promor: Proteomics Data Analysis and Modeling Tools

A comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).

Version: 0.2.1
Depends: R (≥ 3.5.0)
Imports: reshape2, ggplot2, ggrepel, gridExtra, limma, statmod, pcaMethods, VIM, missForest, caret, kernlab, xgboost, naivebayes, viridis, pROC
Suggests: covr, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-07-17
DOI: 10.32614/CRAN.package.promor
Author: Chathurani Ranathunge ORCID iD [aut, cre, cph]
Maintainer: Chathurani Ranathunge <caranathunge86 at>
License: LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.1)]
NeedsCompilation: no
Language: en-US
Citation: promor citation info
Materials: README NEWS
CRAN checks: promor results


Reference manual: promor.pdf
Vignettes: Introduction to promor


Package source: promor_0.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): promor_0.2.1.tgz, r-oldrel (arm64): promor_0.2.1.tgz, r-release (x86_64): promor_0.2.1.tgz, r-oldrel (x86_64): promor_0.2.1.tgz
Old sources: promor archive


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