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dr. M.S. (Matthijs) Berends
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`AMR` is a free and open-source [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any table format, including WHONET/EARS-Net data.
After installing this package, R knows almost all ~20.000 microorganisms and ~500 antibiotics by name and code, and knows all about valid RSI and MIC values.
We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG).
This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but **not** patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation. Read the full license [here](./LICENSE-text.html).
This package can be used for:
* Calculating antimicrobial resistance
* Predicting antimicrobial resistance using regression models
* Predicting empiric susceptibility of both mono therapy and combination therapy
* Predicting future antimicrobial resistance using regression models
* Getting properties for any microorganism (like Gram stain, species, genus or family)
* Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name)
* Plotting antimicrobial resistance
@ -104,7 +107,7 @@ Read more about the data from ITIS [in our manual](./reference/ITIS.html).
The `AMR` package basically does four important things:
1. It **cleanses existing data** by providing new *classes* for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). With this package, you learn R everything about microbiology that is needed for analysis. These functions all use artificial intelligence to guess results that you would expect:
1. It **cleanses existing data** by providing new *classes* for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use artificial intelligence to guess results that you would expect:
* Use `as.mo()` to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" or "esccol" and tries to find expected results using artificial intelligence (AI) on the included ITIS data set, consisting of almost 20,000 microorganisms. It is *very* fast, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
* Use `as.rsi()` to transform values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".