amR is a metapackage that provides a single installation point for the amR suite of packages for antimicrobial resistance (AMR) prediction in bacterial pathogens.
The amR suite consists of three packages that work together:
| Package | Description | Repository |
|---|---|---|
| amRdata | Data curation and feature extraction from bacterial genomes | JRaviLab/amRdata |
| amRml | Machine learning models for AMR prediction | JRaviLab/amRml |
| amRshiny | Interactive dashboard for exploring results | JRaviLab/amRshiny |
# Install amR metapackage
if (!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("JRaviLab/amR")
# Then install all packages in the suite
library(amR)
installAMR()You can also install packages individually:
remotes::install_github("JRaviLab/amRdata")
remotes::install_github("JRaviLab/amRml")
remotes::install_github("JRaviLab/amRshiny")# Load all packages
library(amRdata)
library(amRml)
library(amRshiny)
# 1. Prepare data with amRdata
# features <- prepareFeatures(...)
# 2. Train ML models with amRml
# results <- runMLPipeline(...)
# 3. Explore results with amRshiny
# launchDashboard(...)┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ amRdata │ --> │ amRml │ --> │ amRshiny │
│ │ │ │ │ │
│ - Genomes │ │ - Train LR │ │ - Dashboard │
│ - Features │ │ - Evaluate │ │ - Plots │
│ - Metadata │ │ - Top feats │ │ - Export │
└─────────────┘ └─────────────┘ └─────────────┘
If you use the amR suite in your research, please cite:
Brenner EP^, Ghosh A^, Wolfe EP, Boyer EA, Vang CK, Lesiyon RL, Mayer DA, Ravi J. (2026).
amR: An R package suite for antimicrobial resistance prediction in bacterial pathogens.
https://github.com/JRaviLab/amR
Looking for a cool application of this amR prediction framework? Check out our recent work on predicting AMR in ESKAPE pathogens: Ghosh^, Brenner^, Vang^, Wolfe^, et al., bioRxiv 2025.
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
BSD 3-Clause License. See LICENSE for details.
Corresponding author: Janani Ravi (janani.ravi@cuanschutz.edu)
Lab website: https://jravilab.github.io
Please note that amR is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.