final2

R/aa_globals.R

@@ -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",

R/antibiogram.R

@@ -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 +

R/mo_property.R

@@ -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*.
 #'

R/tidymodels.R

@@ -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(