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(v3.0.1.9083) website
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Package: AMR
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Version: 3.0.1.9082
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Version: 3.0.1.9083
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Date: 2026-07-03
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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NEWS.md
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NEWS.md
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# AMR 3.0.1.9082
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# AMR 3.0.1.9083
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Planned as v3.1.0, end of June 2026.
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# The `AMR` Package for R <a href="https://amr-for-r.org/"><img src="./logo.svg" align="right" height="139" /></a>
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* Provides an **all-in-one solution** for antimicrobial resistance (AMR) data analysis in a One Health approach
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* **Peer-reviewed**, used in over 175 countries, cites over 100 times, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
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* **Peer-reviewed**, used in over 175 countries, cited over 100 times, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
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* Generates **antibiograms** - WISCA for empiric coverage estimates, or traditional/syndromic for AMR surveillance
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* Provides the **full microbiological taxonomy** of `r AMR:::format_included_data_number(AMR::microorganisms)` distinct species and extensive info of `r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial drugs
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* Applies **CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`** and **EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`** clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation
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## Introduction
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The `AMR` package is a peer-reviewed, [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of [many different researchers](./authors.html) from around the globe to make this a successful and durable project!
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This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
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The `AMR` package is a peer-reviewed, [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of [many different researchers](./authors.html) from around the globe to make this a successful and durable project! The `AMR` package was already cited [over 100 times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC) in scientific research.
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After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](./reference/microorganisms.html) (updated `r format(AMR:::TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](./reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).
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With the help of contributors from all corners of the world, the `AMR` package is available in `r lang_txt`. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
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The `AMR` package was cited [over 100 times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC) in scientific research.
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## Practical examples
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### Filtering and selecting data
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index.md
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index.md
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- Provides an **all-in-one solution** for antimicrobial resistance (AMR)
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data analysis in a One Health approach
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- **Peer-reviewed**, used in over 175 countries, cites over 100 times,
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- **Peer-reviewed**, used in over 175 countries, cited over 100 times,
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available in 28 languages
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- Generates **antibiograms** - WISCA for empiric coverage estimates, or
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traditional/syndromic for AMR surveillance
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@@ -51,14 +51,10 @@ and reproducible AMR data analysis, that can therefore empower
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epidemiological analyses to continuously enable surveillance and
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treatment evaluation in any setting. We are a team of [many different
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researchers](./authors.html) from around the globe to make this a
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successful and durable project!
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This work was published in the Journal of Statistical Software (Volume
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104(3); [DOI
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10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and
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formed the basis of two PhD theses ([DOI
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10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and
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[DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
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successful and durable project! The `AMR` package was already cited
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[over 100
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times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC)
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in scientific research.
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After installing this package, R knows [**~97 000 distinct microbial
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species**](./reference/microorganisms.html) (updated mei 2026) and all
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@@ -144,10 +140,6 @@ Urdu, and
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Vietnamese. Antimicrobial drug (group) names and colloquial
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microorganism names are provided in these languages.
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The `AMR` package was cited [over 100
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times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC)
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in scientific research.
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## Practical examples
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### Filtering and selecting data
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@@ -233,7 +225,7 @@ wisca(example_isolates,
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| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
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|:------------------------|:-------------------------------------|:-------------------------------------|
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| 69.9% (64.9-75.3%) | 93.6% (92.1-95%) | 89.9% (87-92.4%) |
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| 70.1% (65.1-75.4%) | 93.6% (92.1-95%) | 89.8% (87.3-92.4%) |
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WISCA supports stratification by any clinical variable, so you can
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generate syndrome-specific or ward-specific coverage estimates:
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| Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
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|:----------------|:------------------------|:-------------------------------------|:-------------------------------------|
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| Clinical | 74.5% (68.6-80.5%) | 93.7% (91.7-95.1%) | 90.4% (87.1-93.1%) |
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| ICU | 57% (48.6-65.6%) | 86.7% (83.3-89.9%) | 83% (78.1-87.5%) |
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| Outpatient | 57.4% (46-69.1%) | 76.7% (70.5-82.7%) | 67.7% (57.3-77.4%) |
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| Clinical | 74.4% (68.2-79.9%) | 93.6% (91.9-95.1%) | 90.4% (86.9-93.3%) |
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| ICU | 57% (48.6-65.9%) | 86.8% (83.4-89.8%) | 82.9% (77.5-87.1%) |
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| Outpatient | 57.5% (45.9-69.3%) | 76.6% (70.6-82.3%) | 67.9% (57.6-77.2%) |
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**For AMR surveillance**, traditional antibiograms remain the right tool
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for tracking resistance per species over time:
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