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Markdown
496 lines
23 KiB
Markdown
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<!-- index.md is generated from index.Rmd; please edit that file. -->
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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)
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data analysis in a One Health approach
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- Peer-reviewed, used in over 175 countries, available in 20 languages
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- Generates **antibiograms** - traditional, combined, syndromic, and
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even WISCA
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- Provides the **full microbiological taxonomy** of ~79 000 distinct
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species and extensive info of ~620 antimicrobial drugs
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- Applies **CLSI 2011-2025** and **EUCAST 2011-2025** clinical and
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veterinary breakpoints, and ECOFFs, for MIC and disk zone
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interpretation
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- Corrects for duplicate isolates, **calculates** and **predicts** AMR
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per antimicrobial class
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- Integrates with **WHONET**, ATC, **EARS-Net**, PubChem, **LOINC**,
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**SNOMED CT**, and **NCBI**
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- 100% free of costs and dependencies, highly suitable for places with
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**limited resources**
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> Now available for Python too! [Click
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> here](./articles/AMR_for_Python.html) to read more.
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<div style="display: flex; font-size: 0.8em;">
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<p style="text-align:left; width: 50%;">
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<small><a href="https://amr-for-r.org/">https://amr-for-r.org</a></small>
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</p>
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<p style="text-align:right; width: 50%;">
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<small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small>
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</p>
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</div>
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<a href="./reference/clinical_breakpoints.html#response-from-clsi-and-eucast"><img src="./endorsement_clsi_eucast.jpg" class="endorse_img" align="right" height="120" /></a>
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------------------------------------------------------------------------
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### Introduction
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The `AMR` package is a peer-reviewed, [free and open-source](#copyright)
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R package with [zero
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dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify
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the analysis and prediction of Antimicrobial Resistance (AMR) and to
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work with microbial and antimicrobial data and properties, by using
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evidence-based methods. **Our aim is to provide a standard** for clean
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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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After installing this package, R knows [**~79 000 distinct microbial
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species**](./reference/microorganisms.html) (updated June 2024) and all
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[**~620 antimicrobial and antiviral
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drugs**](./reference/antimicrobials.html) by name and code (including
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ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all
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about valid SIR and MIC values. The integral clinical breakpoint
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guidelines from CLSI 2011-2025 and EUCAST 2011-2025 are included, even
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with epidemiological cut-off (ECOFF) values. It supports and can read
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any data format, including WHONET data. This package works on Windows,
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macOS and Linux with all versions of R since R-3.0 (April 2013). **It
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was designed to work in any setting, including those with very limited
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resources**. It was created for both routine data analysis and academic
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research at the Faculty of Medical Sciences of the [University of
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Groningen](https://www.rug.nl) and the [University Medical Center
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Groningen](https://www.umcg.nl).
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##### Used in over 175 countries, available in 20 languages
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<a href="./countries_large.png" target="_blank"><img src="./countries.png" align="right" style="max-width: 300px;" /></a>
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Since its first public release in early 2018, this R package has been
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used in almost all countries in the world. Click the map to enlarge and
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to see the country names.
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With the help of contributors from all corners of the world, the `AMR`
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package is available in
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<img src="lang_en.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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English,
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<img src="lang_cs.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Czech,
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<img src="lang_zh.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Chinese,
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<img src="lang_da.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Danish,
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<img src="lang_nl.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Dutch,
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<img src="lang_fi.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Finnish,
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<img src="lang_fr.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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French,
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<img src="lang_de.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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German,
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<img src="lang_el.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Greek,
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<img src="lang_it.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Italian,
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<img src="lang_ja.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Japanese,
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<img src="lang_no.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Norwegian,
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<img src="lang_pl.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Polish,
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<img src="lang_pt.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Portuguese,
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<img src="lang_ro.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Romanian,
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<img src="lang_ru.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Russian,
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<img src="lang_es.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Spanish,
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<img src="lang_sv.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Swedish,
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<img src="lang_tr.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Turkish, and
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<img src="lang_uk.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;">
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Ukrainian. Antimicrobial drug (group) names and colloquial microorganism
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names are provided in these languages.
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### Practical examples
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#### Filtering and selecting data
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One of the most powerful functions of this package, aside from
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calculating and plotting AMR, is selecting and filtering based on
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antimicrobial columns. This can be done using the so-called
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[antimicrobial
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selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html),
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which work in base R, `dplyr` and `data.table`.
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``` r
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# AMR works great with dplyr, but it's not required or neccesary
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library(AMR)
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library(dplyr, warn.conflicts = FALSE)
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example_isolates %>%
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mutate(bacteria = mo_fullname()) %>%
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# filtering functions for microorganisms:
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filter(mo_is_gram_negative(),
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mo_is_intrinsic_resistant(ab = "cefotax")) %>%
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# antimicrobial selectors:
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select(bacteria,
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aminoglycosides(),
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carbapenems())
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#> ℹ Using column 'mo' as input for mo_fullname()
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#> ℹ Using column 'mo' as input for mo_is_gram_negative()
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#> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant()
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#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023). This note will be shown once per session.
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#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
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#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
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#> # A tibble: 35 × 7
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#> bacteria GEN TOB AMK KAN IPM MEM
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#> <chr> <sir> <sir> <sir> <sir> <sir> <sir>
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#> 1 Pseudomonas aeruginosa I S NA R S NA
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#> 2 Pseudomonas aeruginosa I S NA R S NA
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#> 3 Pseudomonas aeruginosa I S NA R S NA
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#> 4 Pseudomonas aeruginosa S S S R NA S
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#> 5 Pseudomonas aeruginosa S S S R S S
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#> 6 Pseudomonas aeruginosa S S S R S S
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#> 7 Stenotrophomonas maltophilia R R R R R R
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#> 8 Pseudomonas aeruginosa S S S R NA S
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#> 9 Pseudomonas aeruginosa S S S R NA S
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#> 10 Pseudomonas aeruginosa S S S R S S
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#> # ℹ 25 more rows
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```
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With only having defined a row filter on Gram-negative bacteria with
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intrinsic resistance to cefotaxime (`mo_is_gram_negative()` and
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`mo_is_intrinsic_resistant()`) and a column selection on two antibiotic
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groups (`aminoglycosides()` and `carbapenems()`), the reference data
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about [all microorganisms](./reference/microorganisms.html) and [all
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antimicrobials](./reference/antimicrobials.html) in the `AMR` package
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make sure you get what you meant.
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#### Generating antibiograms
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The `AMR` package supports generating traditional, combined, syndromic,
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and even weighted-incidence syndromic combination antibiograms (WISCA).
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If used inside [R Markdown](https://rmarkdown.rstudio.com) or
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[Quarto](https://quarto.org), the table will be printed in the right
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output format automatically (such as markdown, LaTeX, HTML, etc.).
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``` r
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antibiogram(example_isolates,
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antimicrobials = c(aminoglycosides(), carbapenems()))
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#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
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#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
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#> ℹ 502 combinations had less than minimum = 30 results and were ignored
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```
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| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
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|:---|:---|:---|:---|:---|:---|:---|
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| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
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| E. coli | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
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| E. faecalis | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | | 0% (0-9%,N=39) |
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| K. pneumoniae | | 90% (79-96%,N=58) | 100% (93-100%,N=51) | | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
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| P. aeruginosa | | 100% (88-100%,N=30) | | 0% (0-12%,N=30) | | 100% (88-100%,N=30) |
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| P. mirabilis | | 94% (80-99%,N=34) | 94% (79-99%,N=32) | | | 94% (80-99%,N=34) |
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| S. aureus | | 99% (97-100%,N=233) | | | | 98% (92-100%,N=86) |
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| S. epidermidis | 0% (0-8%,N=44) | 79% (71-85%,N=163) | | 0% (0-8%,N=44) | | 51% (40-61%,N=89) |
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| S. hominis | | 92% (84-97%,N=80) | | | | 85% (74-93%,N=62) |
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| S. pneumoniae | 0% (0-3%,N=117) | 0% (0-3%,N=117) | | 0% (0-3%,N=117) | | 0% (0-3%,N=117) |
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In combination antibiograms, it is clear that combined antimicrobials
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yield higher empiric coverage:
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``` r
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antibiogram(example_isolates,
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antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
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mo_transform = "gramstain")
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#> ℹ 3 combinations had less than minimum = 30 results and were ignored
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```
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| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
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|:---|:---|:---|:---|
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| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) |
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| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) |
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Like many other functions in this package, `antibiogram()` comes with
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support for 20 languages that are often detected automatically based on
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system language:
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``` r
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antibiogram(example_isolates,
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antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
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mo_transform = "gramstain",
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ab_transform = "name",
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language = "uk") # Ukrainian
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#> ℹ 3 combinations had less than minimum = 30 results and were ignored
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```
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| Збудник | Гентаміцин | Тобраміцин | Ципрофлоксацин |
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|:--------------|:--------------------|:-------------------|:-------------------|
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| Грамнегативні | 96% (95-98%,N=684) | 96% (94-97%,N=686) | 91% (88-93%,N=684) |
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| Грампозитивні | 63% (60-66%,N=1170) | 34% (31-38%,N=665) | 77% (74-80%,N=724) |
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#### Interpreting and plotting MIC and SIR values
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The `AMR` package allows interpretation of MIC and disk diffusion values
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based on CLSI and EUCAST. Moreover, the `ggplot2` package is extended
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with new scale functions, to allow plotting of log2-distributed MIC
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values and SIR values.
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``` r
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library(ggplot2)
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library(AMR)
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# generate some random values
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some_mic_values <- random_mic(size = 100)
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some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
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interpretation <- as.sir(some_mic_values,
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guideline = "EUCAST 2024",
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mo = "E. coli", # or any code or name resembling a known species
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ab = "Cipro") # or any code or name resembling an antibiotic
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# create the plot
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ggplot(data.frame(mic = some_mic_values,
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group = some_groups,
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sir = interpretation),
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aes(x = group, y = mic, colour = sir)) +
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theme_minimal() +
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geom_boxplot(fill = NA, colour = "grey") +
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geom_jitter(width = 0.25) +
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# NEW scale function: plot MIC values to x, y, colour or fill
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scale_y_mic() +
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# NEW scale function: write out S/I/R in any of the 20 supported languages
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# and set colourblind-friendly colours
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scale_colour_sir()
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```
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<a href="./reference/plotting.html" title="Plotting Helpers for AMR Data Analysis">
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<img src="./plot_readme.png" style="width: 1400px; max-width: 100%;">
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</a>
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#### Calculating resistance per group
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For a manual approach, you can use the `resistance` or
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`susceptibility()` function:
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``` r
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example_isolates %>%
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# group by ward:
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group_by(ward) %>%
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# calculate AMR using resistance() for gentamicin and tobramycin
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# and get their 95% confidence intervals using sir_confidence_interval():
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summarise(across(c(GEN, TOB),
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list(total_R = resistance,
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conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
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#> # A tibble: 3 × 5
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#> ward GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int
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#> <chr> <dbl> <chr> <dbl> <chr>
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#> 1 Clinical 0.2289362 0.205-0.254 0.3147503 0.284-0.347
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#> 2 ICU 0.2902655 0.253-0.33 0.4004739 0.353-0.449
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#> 3 Outpatient 0.2 0.131-0.285 0.3676471 0.254-0.493
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```
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Or use [antimicrobial
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selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html)
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to select a series of antibiotic columns:
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``` r
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library(AMR)
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library(dplyr)
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out <- example_isolates %>%
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# group by ward:
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group_by(ward) %>%
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# calculate AMR using resistance(), over all aminoglycosides and polymyxins:
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summarise(across(c(aminoglycosides(), polymyxins()),
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resistance))
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#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
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#> ℹ For polymyxins() using column 'COL' (colistin)
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#> Warning: There was 1 warning in `summarise()`.
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#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.
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#> ℹ In group 3: `ward = "Outpatient"`.
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#> Caused by warning:
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#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" (minimum = 30).
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out
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#> # A tibble: 3 × 6
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#> ward GEN TOB AMK KAN COL
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#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
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#> 1 Clinical 0.2289362 0.3147503 0.6258993 1 0.7802956
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#> 2 ICU 0.2902655 0.4004739 0.6624473 1 0.8574144
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#> 3 Outpatient 0.2 0.3676471 0.6052632 NA 0.8888889
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```
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``` r
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# transform the antibiotic columns to names:
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out %>% set_ab_names()
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#> # A tibble: 3 × 6
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#> ward gentamicin tobramycin amikacin kanamycin colistin
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#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
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#> 1 Clinical 0.2289362 0.3147503 0.6258993 1 0.7802956
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#> 2 ICU 0.2902655 0.4004739 0.6624473 1 0.8574144
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#> 3 Outpatient 0.2 0.3676471 0.6052632 NA 0.8888889
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```
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``` r
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# transform the antibiotic column to ATC codes:
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out %>% set_ab_names(property = "atc")
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#> # A tibble: 3 × 6
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#> ward J01GB03 J01GB01 J01GB06 J01GB04 J01XB01
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#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
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#> 1 Clinical 0.2289362 0.3147503 0.6258993 1 0.7802956
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#> 2 ICU 0.2902655 0.4004739 0.6624473 1 0.8574144
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#> 3 Outpatient 0.2 0.3676471 0.6052632 NA 0.8888889
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```
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### What else can you do with this package?
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This package was intended as a comprehensive toolbox for integrated AMR
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data analysis. This package can be used for:
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- Reference for the taxonomy of microorganisms, since the package
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contains all microbial (sub)species from the List of Prokaryotic names
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with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the
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Global Biodiversity Information Facility
|
||
([GBIF](https://www.gbif.org))
|
||
([manual](./reference/mo_property.html))
|
||
- Interpreting raw MIC and disk diffusion values, based on any CLSI or
|
||
EUCAST guideline ([manual](./reference/as.sir.html))
|
||
- Retrieving antimicrobial drug names, doses and forms of administration
|
||
from clinical health care records
|
||
([manual](./reference/ab_from_text.html))
|
||
- Determining first isolates to be used for AMR data analysis
|
||
([manual](./reference/first_isolate.html))
|
||
- Calculating antimicrobial resistance ([tutorial](./articles/AMR.html))
|
||
- Determining multi-drug resistance (MDR) / multi-drug resistant
|
||
organisms (MDRO) ([tutorial](./articles/MDR.html))
|
||
- Calculating (empirical) susceptibility of both mono therapy and
|
||
combination therapies ([tutorial](./articles/AMR.html))
|
||
- Apply AMR function in predictive modelling
|
||
([tutorial](./articles/AMR_with_tidymodels.html))
|
||
- Getting properties for any microorganism (like Gram stain, species,
|
||
genus or family) ([manual](./reference/mo_property.html))
|
||
- Getting properties for any antimicrobial (like name, code of
|
||
EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)
|
||
([manual](./reference/ab_property.html))
|
||
- Plotting antimicrobial resistance ([tutorial](./articles/AMR.html))
|
||
- Applying EUCAST expert rules ([manual](./reference/eucast_rules.html))
|
||
- Getting SNOMED codes of a microorganism, or getting properties of a
|
||
microorganism based on a SNOMED code
|
||
([manual](./reference/mo_property.html))
|
||
- Getting LOINC codes of an antibiotic, or getting properties of an
|
||
antibiotic based on a LOINC code
|
||
([manual](./reference/ab_property.html))
|
||
- Machine reading the EUCAST and CLSI guidelines from 2011-2021 to
|
||
translate MIC values and disk diffusion diameters to SIR
|
||
([link](./articles/datasets.html))
|
||
- Principal component analysis for AMR ([tutorial](./articles/PCA.html))
|
||
|
||
### Get this package
|
||
|
||
#### Latest official version
|
||
|
||
[](https://cran.r-project.org/package=AMR)
|
||
[](https://cran.r-project.org/package=AMR)
|
||
|
||
This package is available [here on the official R network
|
||
(CRAN)](https://cran.r-project.org/package=AMR). Install this package in
|
||
R from CRAN by using the command:
|
||
|
||
``` r
|
||
install.packages("AMR")
|
||
```
|
||
|
||
It will be downloaded and installed automatically. For RStudio, click on
|
||
the menu *Tools* \> *Install Packages…* and then type in “AMR” and press
|
||
<kbd>Install</kbd>.
|
||
|
||
**Note:** Not all functions on this website may be available in this
|
||
latest release. To use all functions and data sets mentioned on this
|
||
website, install the latest beta version.
|
||
|
||
#### Latest beta version
|
||
|
||
[](https://github.com/msberends/AMR/actions/workflows/check-old-tinytest.yaml)
|
||
[](https://github.com/msberends/AMR/actions/workflows/check-current-testthat.yaml)
|
||
[](https://www.codefactor.io/repository/github/msberends/amr)
|
||
[](https://codecov.io/gh/msberends/AMR?branch=main)
|
||
|
||
Please read our [Developer Guideline
|
||
here](https://github.com/msberends/AMR/wiki/Developer-Guideline).
|
||
|
||
To install the latest and unpublished beta version:
|
||
|
||
``` r
|
||
install.packages("AMR", repos = "beta.amr-for-r.org")
|
||
|
||
# if this does not work, try to install directly from GitHub using the 'remotes' package:
|
||
remotes::install_github("msberends/AMR")
|
||
```
|
||
|
||
### Get started
|
||
|
||
To find out how to conduct AMR data analysis, please [continue reading
|
||
here to get started](./articles/AMR.html) or click a link in the [‘How
|
||
to’ menu](./articles/).
|
||
|
||
### Partners
|
||
|
||
The initial development of this package was part of, related to, or made
|
||
possible by the following non-profit organisations and initiatives:
|
||
|
||
<div align="center">
|
||
|
||
<a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.svg" style="max-width: 200px;"></a>
|
||
<a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.svg" style="max-width: 200px;"></a>
|
||
<a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation"><img src="./logo_certe.svg" style="max-width: 200px;"></a>
|
||
<a href="https://www.deutschland-nederland.eu" title="EurHealth-1-Health"><img src="./logo_eh1h.png" style="max-width: 200px;"></a>
|
||
<a href="https://www.deutschland-nederland.eu" title="INTERREG"><img src="./logo_interreg.png" style="max-width: 200px;"></a>
|
||
|
||
</div>
|
||
|
||
### Copyright
|
||
|
||
This R package is free, open-source software and licensed under the [GNU
|
||
General Public License v2.0 (GPL-2)](./LICENSE-text.html). In a
|
||
nutshell, this means that this package:
|
||
|
||
- May be used for commercial purposes
|
||
|
||
- May be used for private purposes
|
||
|
||
- May **not** be used for patent purposes
|
||
|
||
- May be modified, although:
|
||
|
||
- Modifications **must** be released under the same license when
|
||
distributing the package
|
||
- Changes made to the code **must** be documented
|
||
|
||
- May be distributed, although:
|
||
|
||
- Source code **must** be made available when the package is
|
||
distributed
|
||
- A copy of the license and copyright notice **must** be included with
|
||
the package.
|
||
|
||
- Comes with a LIMITATION of liability
|
||
|
||
- Comes with NO warranty
|