(v2.1.1.9143) unit tests

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 2.1.1.9142
+Version: 2.1.1.9143
 Date: 2025-02-07
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9142
+# AMR 2.1.1.9143
 
 *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
 

PythonPackage/AMR/AMR.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.2
 Name: AMR
-Version: 2.1.1.9142
+Version: 2.1.1.9143
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends

PythonPackage/AMR/setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='AMR',
-    version='2.1.1.9142',
+    version='2.1.1.9143',
     packages=find_packages(),
     install_requires=[
         'rpy2',

R/aa_globals.R

@@ -138,12 +138,15 @@ globalVariables(c(
   "atc_group1",
   "atc_group2",
   "base_ab",
+  "beta_posterior_1",
+  "beta_posterior_2",
   "ci_max",
   "ci_min",
   "clinical_breakpoints",
   "code",
   "cols",
   "count",
+  "coverage",
   "data",
   "disk",
   "dosage",
@@ -152,6 +155,7 @@ globalVariables(c(
   "fullname",
   "fullname_lower",
   "g_species",
+  "gamma_posterior",
   "genus",
   "gr",
   "group",
@@ -166,6 +170,7 @@ globalVariables(c(
   "language",
   "lookup",
   "lower",
+  "lower_ci",
   "method",
   "mic ",
   "mic",
@@ -197,9 +202,11 @@ globalVariables(c(
   "species",
   "syndromic_group",
   "total",
+  "total_rows",
   "txt",
   "type",
   "upper",
+  "upper_ci",
   "uti_index",
   "value",
   "varname",

R/antibiogram.R

@@ -625,7 +625,7 @@ antibiogram.default <- function(x,
 
         # simulate pathogen incidence
         # = Dirichlet (Gamma) parameters
-        random_incidence <- runif(1, min = 0, max = 1)
+        random_incidence <- stats::runif(1, min = 0, max = 1)
         simulated_incidence <- stats::qgamma(
           p = random_incidence,
           shape = priors$gamma_posterior,
@@ -636,7 +636,7 @@ antibiogram.default <- function(x,
 
         # simulate susceptibility
         # = Beta parameters
-        random_susceptibity <- runif(1, min = 0, max = 1)
+        random_susceptibity <- stats::runif(1, min = 0, max = 1)
         simulated_susceptibility <- stats::qbeta(
           p = random_susceptibity,
           shape1 = priors$beta_posterior_1,
@@ -1009,7 +1009,7 @@ wisca <- function(x,
 }
 
 #' @export
-#' @param wisca_model the outcome of [wisca()] or [antibiogram(..., wisca = TRUE)]
+#' @param wisca_model the outcome of [wisca()] or [`antibiogram(..., wisca = TRUE)`][antibiogram()]
 #' @rdname antibiogram
 retrieve_wisca_parameters <- function(wisca_model, ...) {
   stop_ifnot(isTRUE(attributes(wisca_model)$wisca), "This function only applies to WISCA models. Use `wisca()` or `antibiogram(..., wisca = TRUE)` to create a WISCA model.")

data-raw/gpt_training_text_v2.1.1.9142.txt → data-raw/gpt_training_text_v2.1.1.9143.txt

@@ -1,6 +1,6 @@
 This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
 
-First and foremost, you are trained on version 2.1.1.9142. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9143. Remember this whenever someone asks which AMR package version you’re at.
 
 Below are the contents of the  file, the  file, and all the  files (documentation) in the package. Every file content is split using 100 hypens.
 ----------------------------------------------------------------------------------------------------
@@ -1708,7 +1708,7 @@ retrieve_wisca_parameters(wisca_model, ...)
 
 \item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
 
-\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \link{antibiogram(..., wisca = TRUE)}}
+\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}}
 
 \item{...}{when used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use)}