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 [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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=promor to link to this page.
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