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dr. M.S. (Matthijs) Berends
2026-06-23 01:20:09 +02:00
parent 57e45db320
commit f0c3ce80cd
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2
DESCRIPTION
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@@ -1,6 +1,6 @@
Package: AMR
Version: 3.0.1.9059
Date: 2026-05-06
Date: 2026-06-23
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by

1
NEWS.md
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@@ -51,6 +51,7 @@ Planned as v3.1.0, end of June 2026.
* Improved console messages with clickable links throughout, powered by `cli` if it is installed (#191, #265)
* `as.disk()`: input validation is now more strict, rejecting values that are not recognisable as a numeric disk zone diameter
# AMR 3.0.1
This is a bugfix release following the release of v3.0.0 in June 2025.

7
R/aa_globals.R
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@@ -113,7 +113,7 @@ TAXONOMY_VERSION <- list(
LPSN = list(
name = "List of Prokaryotic names with Standing in Nomenclature (LPSN)",
accessed_date = as.Date("2026-05-07"),
citation = "Freese, HM *et al.* (2026). **TYGS and LPSN in 2025: a Global Core Biodata Resource for genome-based classification and nomenclature of prokaryotes within DSMZ Digital Diversity.** Nucleic Acids Research, 54, D884–D891; \\doi{10.1093/nar/gkaf1110}.",
citation = "Freese, HM *et al.* (2026). **TYGS and LPSN in 2025: a Global Core Biodata Resource for genome-based classification and nomenclature of prokaryotes within DSMZ Digital Diversity.** Nucleic Acids Research, 54, D884\u2013D891; \\doi{10.1093/nar/gkaf1110}.",
url = "https://lpsn.dsmz.de"
),
MycoBank = list(
@@ -149,10 +149,13 @@ TAXONOMY_VERSION <- list(
)
globalVariables(c(
".coverage",
".GenericCallEnv",
".lower",
".mo",
".rowid",
".syndromic_group",
".upper",
"ab",
"ab_txt",
"affect_ab_name",
@@ -188,6 +191,7 @@ globalVariables(c(
"hjust",
"host_index",
"host_match",
"incidence",
"input",
"input_given",
"intrinsic_resistant",
@@ -215,6 +219,7 @@ globalVariables(c(
"old",
"old_name",
"p_susceptible",
"pathogen",
"pattern",
"R",
"rank_index",

6
R/antibiogram.R
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@@ -898,7 +898,7 @@ antibiogram.default <- function(x,
susceptibility = sim_susceptibility
)
out_wisca$coverage[out_wisca$group == group] <- mean(sim_coverage)
ci_vals <- unname(stats::quantile(coverage_simulations, probs = probs))
ci_vals <- unname(stats::quantile(sim_coverage, probs = probs))
out_wisca$lower_ci[out_wisca$group == group] <- ci_vals[1]
out_wisca$upper_ci[out_wisca$group == group] <- ci_vals[2]
}
@@ -1657,10 +1657,10 @@ autoplot.antibiogram <- function(object,
if (is.null(caption)) {
if (is_wisca) {
out <- out + labs(caption = "Overlapping credible intervals:\nclinically non-inferior (Bielicki 2020)")
out <- out + ggplot2::labs(caption = "Overlapping credible intervals:\nclinically non-inferior (Bielicki 2020)")
}
} else if (!caption %in% c(FALSE, NA)) {
out <- out + labs(caption = caption)
out <- out + ggplot2::labs(caption = caption)
}
out <- out +

2
R/mo_property.R
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@@ -46,7 +46,7 @@
#'
#' The short name ([mo_shortname()]) returns the first character of the genus and the full species, such as `"E. coli"`, for species and subspecies. Exceptions are abbreviations of staphylococci (such as *"CoNS"*, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as *"GBS"*, Group B Streptococci). Please bear in mind that e.g. *E. coli* could mean *Escherichia coli* (kingdom of Bacteria) as well as *Entamoeba coli* (kingdom of Protozoa). Returning to the full name will be done using [as.mo()] internally, giving priority to bacteria and human pathogens, i.e. `"E. coli"` will always be considered *Escherichia coli*. As a result, `mo_fullname(mo_shortname("Entamoeba coli"))` returns `"Escherichia coli"`.
#'
#' Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G\u00f6ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), [mo_kingdom()] and [mo_domain()] return different results for bacteria and archaea: [mo_kingdom()] returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while [mo_domain()] returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results.
#' Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G"{o}ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), [mo_kingdom()] and [mo_domain()] return different results for bacteria and archaea: [mo_kingdom()] returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while [mo_domain()] returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results.
#'
#' Determination of human pathogenicity ([mo_pathogenicity()]) is strongly based on Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}). This function returns a [factor] with the levels *Pathogenic*, *Potentially pathogenic*, *Non-pathogenic*, and *Unknown*.
#'

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R/sysdata.rda
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R/tidymodels.R
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@@ -120,13 +120,14 @@ all_disk_predictors <- function() {
#' @rdname amr-tidymodels
#' @export
step_mic_log2 <- function(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = recipes::rand_id("mic_log2")) {
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = recipes::rand_id("mic_log2")
) {
recipes::add_step(
recipe,
step_mic_log2_new(
@@ -195,13 +196,14 @@ tidy.step_mic_log2 <- function(x, ...) {
#' @rdname amr-tidymodels
#' @export
step_sir_numeric <- function(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = recipes::rand_id("sir_numeric")) {
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = recipes::rand_id("sir_numeric")
) {
recipes::add_step(
recipe,
step_sir_numeric_new(

2
data-raw/interpretive_rules.tsv
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@@ -1444,7 +1444,7 @@ EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella cephalo
EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella aminoglycosides R
EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella PEF S CIP S
EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella PEF R CIP R
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX R AMC, AMP, AMX, APL, APX, ATM, AXS, AZA, AZD, AZL, BAM, BIA, BNB, BNP, CAC, CAR, CAT, CAZ, CCL, CCP, CCV, CCX, CDC, CDR, CEB, CEC, CED, CEM, CEP, CEQ, CFA, CFM, CFM1, CFP, CFR, CFS, CFZ, CHE, CIC, CID, CLM, CLO, CMX, CMZ, CND, CPA, CPC, CPD, CPI, CPL, CPM, CPO, CPR, CPX, CRB, CRD, CRN, CRO, CSE, CSL, CSU, CTA, CTB, CTC, CTF, CTL, CTS, CTT, CTX, CTZ, CXA, CXM, CZA, CZD, CZL, CZO, CZP, CZT, CZX, DIC, DIT, DIX, DIZ, DOR, EPC, ETP, FDC, FEP, FLC, FOV, FOX, FPE, FPT, FPZ, FTA, HAP, HET, IMR, IPM, LEN, LEX, LOR, LTM, MAN, MEC, MEM, MET, MEV, MEZ, MSU, MTM, NAF, OXA, PAN, PEN, PHE, PHN, PIP, PIS, PME, PNM, PNO, PRB, PRC, PRP, PSU, PVM, RIA, RID, RIT, RZM, SAM, SBC, SLT6, SRX, TAL, TAN, TBP, TCC, TEM, TIC, TIO, TMN, TZP, ZO R Betalactams without ceftaroline and ceftobiprole = betalactams()[!betalactams() %in% c("CPT", "BPR")]
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX R AMC, AMP, AMX, APL, APX, ATM, AXS, AZA, AZD, AZL, BAM, BIA, BNB, BNP, CAC, CAR, CAT, CAZ, CCL, CCP, CCV, CCX, CDC, CDR, CEB, CEC, CED, CEM, CEP, CEQ, CFA, CFM, CFM1, CFP, CFR, CFS, CFZ, CHE, CIC, CID, CLM, CLO, CMX, CMZ, CND, CPA, CPC, CPD, CPI, CPL, CPM, CPO, CPR, CPX, CRB, CRD, CRN, CRO, CSE, CSL, CSU, CTA, CTB, CTC, CTF, CTL, CTS, CTT, CTX, CTZ, CXA, CXM, CZA, CZD, CZL, CZO, CZP, CZT, CZX, DIC, DIT, DIX, DIZ, DOR, EPC, ETP, FDC, FEP, FLC, FOV, FOX, FPE, FPT, FPZ, FTA, HAP, HET, IMR, IPM, LEN, LEX, LOR, LTM, MAN, MEC, MEM, MET, MEV, MEZ, MSU, MTM, NAF, OXA, PAN, PEN, PHE, PHN, PIP, PIS, PME, PNM, PNO, PRB, PRC, PRP, PSU, PVM, RIA, RID, RIT, RZM, SAM, SBC, SLT6, SRX, TAL, TAN, TBP, TCC, TEM, TIC, TIO, TMN, TZP, ZOP R Betalactams without ceftaroline and ceftobiprole = betalactams()[!betalactams() %in% c("CPT", "BPR")]
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX S betalactams_with_inhibitor, carbapenems S Must be S to all betactams with recognised anti-staphylococcal activity
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus PEN R AMP, AMX, AZL, BAM, CRB, CRN, EPC, HET, MEC, MEZ, MTM, PIP, PME, PVM, SBC, TAL, TEM, TIC R all penicillins without beta-lactamse inhibitor and excluding isoxazolylpenicillines
EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus ERY, CLI S macrolides, lincosamides S
Can't render this file because it has a wrong number of fields in line 10.

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data/antibiotics.rda
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data/antimicrobials.rda
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118
index.md
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@@ -26,12 +26,9 @@
<div style="display: flex; font-size: 0.8em;">
<p style="text-align:left; width: 50%;">
<small><a href="https://amr-for-r.org/">amr-for-r.org</a></small>
</p>
<p style="text-align:right; width: 50%;">
<small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">doi.org/10.18637/jss.v104.i03</a></small>
</p>
@@ -174,24 +171,26 @@ example_isolates %>%
#> 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>
#> 1 Pseudomonas aeruginosa I S NA R S NA
#> 2 Pseudomonas aeruginosa I S NA R S NA
#> 3 Pseudomonas aeruginosa I S NA R S NA
#> 4 Pseudomonas aeruginosa S S S R NA S
#> 5 Pseudomonas aeruginosa S S S R S S
#> 6 Pseudomonas aeruginosa S S S R S S
#> 7 Stenotrophomonas maltophilia R R R R R R
#> 8 Pseudomonas aeruginosa S S S R NA S
#> 9 Pseudomonas aeruginosa S S S R NA S
#> 10 Pseudomonas aeruginosa S S S R S S
#> bacteria GEN TOB AMK KAN IPM MEM
#> <chr> <sir> <sir> <sir> <sir> <sir> <sir>
#> 1 Pseudomonas aer… I S NA R S NA
#> 2 Pseudomonas aer… I S NA R S NA
#> 3 Pseudomonas aer… I S NA R S NA
#> 4 Pseudomonas aer… S S S R NA S
#> 5 Pseudomonas aer… S S S R S S
#> 6 Pseudomonas aer… S S S R S S
#> 7 Stenotrophomona… R R R R R R
#> 8 Pseudomonas aer… S S S R NA S
#> 9 Pseudomonas aer… S S S R NA S
#> 10 Pseudomonas aer… S S S R S S
#> # ℹ 25 more rows
```
@@ -215,23 +214,24 @@ output format automatically (such as markdown, LaTeX, HTML, etc.).
``` r
antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems()))
#> 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 For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
```
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:---|:---|:---|:---|:---|:---|:---|
| 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) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------|
| 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) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
In combination antibiograms, it is clear that combined antimicrobials
yield higher empiric coverage:
@@ -242,10 +242,10 @@ antibiogram(example_isolates,
mo_transform = "gramstain")
```
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|:---|
| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) |
| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) |
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:--------------|:------------------------|:-------------------------------------|:-------------------------------------|
| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) |
| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) |
Like many other functions in this package, `antibiogram()` comes with
support for 28 languages that are often detected automatically based on
@@ -318,16 +318,18 @@ example_isolates %>%
summarise(across(c(GEN, TOB),
list(total_R = resistance,
conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
#> category susceptible. Set the `guideline` argument or the `AMR_guideline`
#> option to either "CLSI" or "EUCAST", see `?AMR-options`.
#> ℹ `resistance()` assumes the EUCAST guideline and thus
#> considers the 'I' category susceptible. Set the `guideline`
#> argument or the `AMR_guideline` option to either "CLSI" or
#> "EUCAST", see `?AMR-options`.
#> ℹ This message will be shown once per session.
#> # A tibble: 3 × 5
#> ward GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int
#> <chr> <dbl> <chr> <dbl> <chr>
#> 1 Clinical 0.229 0.205-0.254 0.315 0.284-0.347
#> 2 ICU 0.290 0.253-0.33 0.400 0.353-0.449
#> 3 Outpatient 0.2 0.131-0.285 0.368 0.254-0.493
#> ward GEN_total_R GEN_conf_int TOB_total_R
#> <chr> <dbl> <chr> <dbl>
#> 1 Clinical 0.229 0.205-0.254 0.315
#> 2 ICU 0.290 0.253-0.33 0.400
#> 3 Outpatient 0.2 0.131-0.285 0.368
#> # ℹ 1 more variable: TOB_conf_int <chr>
```
Or use [antimicrobial
@@ -344,15 +346,16 @@ out <- example_isolates %>%
# calculate AMR using resistance(), over all aminoglycosides and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()),
resistance))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `polymyxins()` using column COL (colistin)
#> Warning: There was 1 warning in `summarise()`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()),
#> resistance)`.
#> ℹ In group 3: `ward = "Outpatient"`.
#> Caused by warning:
#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient"
#> (whilst `minimum = 30`).
#> ! Introducing NA: only 23 results available for KAN in group:
#> ward = "Outpatient" (whilst `minimum = 30`).
out
#> # A tibble: 3 × 6
#> ward GEN TOB AMK KAN COL
@@ -366,11 +369,12 @@ out
# transform the antibiotic columns to names:
out %>% set_ab_names()
#> # A tibble: 3 × 6
#> ward gentamicin tobramycin amikacin kanamycin colistin
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Clinical 0.229 0.315 0.626 1 0.780
#> 2 ICU 0.290 0.400 0.662 1 0.857
#> 3 Outpatient 0.2 0.368 0.605 NA 0.889
#> ward gentamicin tobramycin amikacin kanamycin
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Clinical 0.229 0.315 0.626 1
#> 2 ICU 0.290 0.400 0.662 1
#> 3 Outpatient 0.2 0.368 0.605 NA
#> # ℹ 1 more variable: colistin <dbl>
```
``` r

2
man/g.test.Rd
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@@ -46,7 +46,7 @@ A list with class \code{"htest"} containing the following
\code{(observed - expected) / sqrt(expected)}.}
\item{stdres}{standardized residuals,
\code{(observed - expected) / sqrt(V)}, where \code{V} is the
residual cell variance (Agresti, 2007, section 2.4.5
residual cell variance {(\if{html}{\out{<a href="#reference+chisq.test.Rd+R+3AAgresti+3A2007" class="citation">}}Agresti 2007\if{html}{\out{</a>}}, section 2.4.5)}
for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
}
\description{

5
man/ggplot_pca.Rd
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@@ -42,8 +42,9 @@ ggplot_pca(x, choices = 1:2, scale = 1, pc.biplot = TRUE,
}
\item{pc.biplot}{
If true, use what Gabriel (1971) refers to as a "principal component
biplot", with \code{lambda = 1} and observations scaled up by sqrt(n) and
If true, use what {\if{html}{\cite{}\out{<a href="#reference+biplot.princomp.Rd+R+3AGabriel+3A1971" class="citation">}}Gabriel (1971)\if{html}{\out{</a>}}} refers to as a
\dQuote{principal component biplot},
with \code{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.

2
man/mo_property.Rd
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@@ -200,7 +200,7 @@ All functions will, at default, \strong{not} keep old taxonomic properties, as s
The short name (\code{\link[=mo_shortname]{mo_shortname()}}) returns the first character of the genus and the full species, such as \code{"E. coli"}, for species and subspecies. Exceptions are abbreviations of staphylococci (such as \emph{"CoNS"}, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as \emph{"GBS"}, Group B Streptococci). Please bear in mind that e.g. \emph{E. coli} could mean \emph{Escherichia coli} (kingdom of Bacteria) as well as \emph{Entamoeba coli} (kingdom of Protozoa). Returning to the full name will be done using \code{\link[=as.mo]{as.mo()}} internally, giving priority to bacteria and human pathogens, i.e. \code{"E. coli"} will always be considered \emph{Escherichia coli}. As a result, \code{mo_fullname(mo_shortname("Entamoeba coli"))} returns \code{"Escherichia coli"}.
Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G\u00f6ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), \code{\link[=mo_kingdom]{mo_kingdom()}} and \code{\link[=mo_domain]{mo_domain()}} return different results for bacteria and archaea: \code{\link[=mo_kingdom]{mo_kingdom()}} returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while \code{\link[=mo_domain]{mo_domain()}} returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results.
Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G"{o}ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), \code{\link[=mo_kingdom]{mo_kingdom()}} and \code{\link[=mo_domain]{mo_domain()}} return different results for bacteria and archaea: \code{\link[=mo_kingdom]{mo_kingdom()}} returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while \code{\link[=mo_domain]{mo_domain()}} returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results.
Determination of human pathogenicity (\code{\link[=mo_pathogenicity]{mo_pathogenicity()}}) is strongly based on Bartlett \emph{et al.} (2022, \doi{10.1099/mic.0.001269}). This function returns a \link{factor} with the levels \emph{Pathogenic}, \emph{Potentially pathogenic}, \emph{Non-pathogenic}, and \emph{Unknown}.