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
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:

Reference manual:	promor.pdf
Vignettes:	Introduction to promor

Downloads:

Package source:	promor_0.2.1.tar.gz
Windows binaries:	r-devel: promor_0.2.1.zip, r-release: promor_0.2.1.zip, r-oldrel: promor_0.2.1.zip
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

Linking:

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