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: | 0.2.2 |
| 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: | 2025-11-11 |
| DOI: | 10.32614/CRAN.package.promor |
| Author: | Chathurani Ranathunge [aut, cre, cph] |
| Maintainer: | Chathurani Ranathunge <caranathunge86 at gmail.com> |
| BugReports: | https://github.com/caranathunge/promor/issues |
| License: | LGPL-2.1 |LGPL-3 [expanded from: LGPL (≥ 2.1)] |
| URL: | https://github.com/caranathunge/promor,https://caranathunge.github.io/promor/ |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | promor citation info |
| Materials: | README,NEWS |
| CRAN checks: | promor results |
Documentation:
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