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<ahref="#welcome-to-the-amr-package"class="anchor"></a>Welcome to the AMR package</h1>
<p><code>AMR</code> is a free, open-source and independent R package 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. <strong>Our aim is to provide a standard</strong> for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.</p>
<p>After installing this package, R knows <strong>~70,000 distinct microbial species</strong> and all <strong>~550 antibiotic, antimycotic and antiviral drugs</strong> by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
<p>This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). <strong>It was designed to work in any setting, including those with very limited resources</strong>. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained (see Changelog) and is free software (see Copyright).</p>
<p>Since its first public release in early 2018, this package has been downloaded from more than 100 countries.</p>
<li>Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature</li>
<li>Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines</li>
<li>Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records</li>
<li>Determining first isolates to be used for AMR analysis</li>
<p>Developed by <ahref="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <ahref="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <ahref="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <ahref="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <ahref="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <ahref="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
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