#> Groups (n=4, named as 'order'): diff --git a/articles/PCA.md b/articles/PCA.md index cd1865196..5b9e7f33d 100644 --- a/articles/PCA.md +++ b/articles/PCA.md @@ -119,7 +119,7 @@ summary(pca_result) #> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales" #> Importance of components: #> PC1 PC2 PC3 PC4 PC5 PC6 PC7 -#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 +#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 ``` diff --git a/articles/WHONET.html b/articles/WHONET.html index ddcd72528..4c172ad00 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9084 + 3.0.1.9085 diff --git a/articles/WISCA.html b/articles/WISCA.html index b8e78453d..dbddef6a6 100644 --- a/articles/WISCA.html +++ b/articles/WISCA.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9084 + 3.0.1.9085 diff --git a/articles/datasets.html b/articles/datasets.html index eb74b0be7..5fa1c3d34 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9084 + 3.0.1.9085 @@ -441,7 +441,7 @@ column names:ab, cid, name, group, a iv_ddd, iv_units, and loinc. This data set is in R available as antimicrobials, after you load the AMR package. -It was last updated on 9 July 2026 15:14:59 UTC. Find more info about +It was last updated on 9 July 2026 19:05:33 UTC. Find more info about the contents, (scientific) source, and structure of this data set here. Direct download links: diff --git a/articles/datasets.md b/articles/datasets.md index 3d58bde37..fa8514dbd 100644 --- a/articles/datasets.md +++ b/articles/datasets.md @@ -100,7 +100,7 @@ names: This data set is in R available as `antimicrobials`, after you load the `AMR` package. -It was last updated on 9 July 2026 15:14:59 UTC. Find more info about +It was last updated on 9 July 2026 19:05:33 UTC. Find more info about the contents, (scientific) source, and structure of this [data set here](https://amr-for-r.org/reference/antimicrobials.html). diff --git a/articles/index.html b/articles/index.html index 0f5f3f2ed..3165adaef 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9084 + 3.0.1.9085 diff --git a/authors.html b/authors.html index c53f73767..e792ba25c 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9084 + 3.0.1.9085 diff --git a/index.html b/index.html index e5d7db438..81be9b605 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@ AMR (for R) - 3.0.1.9084 + 3.0.1.9085 @@ -111,8 +111,9 @@ Introduction -The AMR package is a peer-reviewed, free and open-source R package with zero dependencies 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 from around the globe to make this a successful and durable project! The AMR package was already cited over 100 times in scientific research. -After installing this package, R knows ~97 000 distinct microbial species (updated May 2026) and all ~620 antimicrobial and antiviral drugs 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 2011-2026 and EUCAST 2011-2026 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 and the University Medical Center Groningen. +The AMR package is a peer-reviewed, free and open-source R package with zero dependencies 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 has always been 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 from around the globe to make this a successful and durable project! The AMR package was already cited over 100 times in scientific research. +After installing this package, R knows ~97 000 distinct microbial species (updated mei 2026) and all ~620 antimicrobial and antiviral drugs 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 2011-2026 and EUCAST 2011-2026 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 and the University Medical Center Groningen. Used in over 175 countries, available in 28 languages @@ -145,11 +146,13 @@ #> ℹ Using column mo as input for `mo_fullname()` #> ℹ Using column mo as input for `mo_is_gram_negative()` #> ℹ Using column mo as input for `mo_is_intrinsic_resistant()` -#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023). -#> This note will be shown once per session. -#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN -#> (kanamycin) -#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) +#> ℹ Determining intrinsic resistance based on 'EUCAST Expected +#> Resistant Phenotypes' v1.2 (2023). This note will be shown +#> once per session. +#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB +#> (tobramycin), AMK (amikacin), and KAN (kanamycin) +#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM +#> (meropenem) #> # A tibble: 35 × 7 #> bacteria GEN TOB AMK KAN IPM MEM #> <chr> <sir> <sir> <sir> <sir> <sir> <sir> @@ -178,9 +181,9 @@ #> Warning: invalid microorganism code, NA generated - - - + + + Piperacillin/tazobactam @@ -188,9 +191,9 @@ Piperacillin/tazobactam + Tobramycin -70% (64.8-75.2%) -93.6% (92-95.1%) -89.9% (87.1-92.5%) +70% (64.8-75.1%) +93.6% (92.1-95%) +89.9% (86.9-92.3%) WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates: @@ -202,10 +205,10 @@ #> Warning: invalid microorganism code, NA generated - - - - + + + + Syndromic Group @@ -216,21 +219,21 @@ Clinical -74.6% (69.3-80.3%) -93.6% (92.1-95%) -90.4% (87-93.2%) +74.7% (69-80.3%) +93.6% (92-95.2%) +90.4% (86.8-93.1%) ICU -56.9% (48.2-66.3%) -86.7% (83.4-89.7%) -82.9% (78.1-87.3%) +56.9% (48.7-66%) +86.8% (83.6-90%) +82.8% (78.3-87.3%) Outpatient -57.3% (45.8-69.1%) -76.6% (70.6-81.9%) -67.9% (58-76.9%) +57.2% (46-68.2%) +76.5% (70.3-82.2%) +67.7% (57.3-77.2%) @@ -239,14 +242,15 @@ antibiogram(example_isolates, mo_transform = "gramstain", antimicrobials = c("AMC", carbapenems(), "TZP")) -#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
This data set is in R available as antimicrobials, after you load the AMR package.
antimicrobials
AMR
It was last updated on 9 July 2026 15:14:59 UTC. Find more info about +
It was last updated on 9 July 2026 19:05:33 UTC. Find more info about the contents, (scientific) source, and structure of this data set here.
Direct download links:
The AMR package is a peer-reviewed, free and open-source R package with zero dependencies 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 from around the globe to make this a successful and durable project! The AMR package was already cited over 100 times in scientific research.
After installing this package, R knows ~97 000 distinct microbial species (updated May 2026) and all ~620 antimicrobial and antiviral drugs 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 2011-2026 and EUCAST 2011-2026 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 and the University Medical Center Groningen.
The AMR package is a peer-reviewed, free and open-source R package with zero dependencies 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 has always been 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 from around the globe to make this a successful and durable project! The AMR package was already cited over 100 times in scientific research.
After installing this package, R knows ~97 000 distinct microbial species (updated mei 2026) and all ~620 antimicrobial and antiviral drugs 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 2011-2026 and EUCAST 2011-2026 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 and the University Medical Center Groningen.
WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates:
antibiogram(example_isolates, mo_transform = "gramstain", antimicrobials = c("AMC", carbapenems(), "TZP")) -#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
Planned as v3.1.0, end of June 2026.
domain
microorganisms
mo_kingdom()
"Pseudomonadati"
"Bacteria"
mo_domain()
kingdom
future
as.sir()
wisca()
future::plan(future::multisession)
parallel = TRUE
clinical_breakpoints
breakpoint_type = "ECOFF"
wisca_plot()
options(AMR_guideline = "EUCAST 2012")
interpretive_rules()
resistance()
susceptibility()
count_resistant()
count_susceptible()
top_n_microorganisms()
property_for_each
property = NULL
The AMR package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
standardized residuals, (observed - expected) / sqrt(V), where V is the - residual cell variance (Agresti, 2007, section 2.4.5 + residual cell variance (Agresti 2007, section 2.4.5) for the case where x is a matrix, n * p * (1 - p) otherwise).
(observed - expected) / sqrt(V)
V
x
n * p * (1 - p)
If true, use what Gabriel (1971) refers to as a "principal component - biplot", with lambda = 1 and observations scaled up by sqrt(n) and +
lambda = 1
If true, use what Gabriel (1971) refers to as a + “principal component biplot”, + with lambda = 1 and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then inner products between variables approximate covariances and distances between observations approximate Mahalanobis distance.