(v2.1.1.9133) update math formulae

.github/workflows/check-current-testthat.yaml

@@ -63,10 +63,10 @@ jobs:
           - {os: ubuntu-latest,  r: 'release', allowfail: false}
 
           # older versions (see also check-old-tinytest.yaml for even older versions):
-          - {os: ubuntu-latest, r: 'oldrel', allowfail: false}
           - {os: ubuntu-latest, r: 'oldrel-1', allowfail: false}
           - {os: ubuntu-latest, r: 'oldrel-2', allowfail: false}
           - {os: ubuntu-latest, r: 'oldrel-3', allowfail: false}
+          - {os: ubuntu-latest, r: 'oldrel-4', allowfail: false}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

PythonPackage/AMR/AMR/datasets.py

@@ -21,7 +21,6 @@ utils = importr('utils')
 base.options(warn = -1)
 
 # Override R library paths globally for the session
-robjects.r(f'.Library <- "{r_lib_path}"')  # Replace default library
 robjects.r(f'.Library.site <- "{r_lib_path}"')  # Replace site-specific library
 base._libPaths(r_lib_path)  # Override .libPaths() as well
 

R/antibiogram.R

@@ -195,48 +195,26 @@
 #' You can also use functions from specific 'table reporting' packages to transform the output of [antibiogram()] to your needs, e.g. with `flextable::as_flextable()` or `gt::gt()`.
 #'
 #' @section Why Use WISCA?:
-#' WISCA, as outlined by Barbieri *et al.* (\doi{10.1186/s13756-021-00939-2}), stands for 
-#' Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability 
-#' of adequate empirical antimicrobial regimen coverage for specific infection syndromes. 
-#' This method leverages a Bayesian hierarchical logistic regression framework with random 
-#' effects for pathogens and regimens, enabling robust estimates in the presence of sparse 
-#' data. 
 #'  
-#' The Bayesian model assumes conjugate priors for parameter estimation. For example, the 
-#' coverage probability \ifelse{latex}{\deqn{$theta$}}{$theta$} for a given antimicrobial regimen 
-#' is modeled using a Beta distribution as a prior: 
+#' WISCA, as outlined by Barbieri *et al.* (\doi{10.1186/s13756-021-00939-2}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data. 
 #'
-#' \ifelse{latex}{\deqn{$theta$ \sim \text{Beta}($alpha$_0, $beta$_0)}}{
-#' \ifelse{html}{\figure{beta_prior.png}{options: width="300" alt="Beta prior"}}{$theta$ ~ Beta($alpha$_0, $beta$_0)}}
+#' The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior: 
 #'
-#' where \eqn{$alpha$_0} and \eqn{$beta$_0} represent prior successes and failures, respectively, 
-#' informed by expert knowledge or weakly informative priors (e.g., \eqn{$alpha$_0 = 1, $beta$_0 = 1}).
+#' \deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
 #'
-#' The likelihood function is constructed based on observed data, where the number of covered 
-#' cases for a regimen follows a binomial distribution:
+#' where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
 #'
-#' \ifelse{latex}{\deqn{y \sim \text{Binomial}(n, $theta$)}}{
-#' \ifelse{html}{\figure{binomial_likelihood.png}{options: width="300" alt="Binomial likelihood"}}{y ~ Binomial(n, $theta$)}}
+#' \deqn{y \sim \text{Binomial}(n, \theta)}
 #'
-#' Posterior parameter estimates are obtained by combining the prior and likelihood using 
-#' Bayes' theorem. The posterior distribution of \eqn{$theta$} is also a Beta distribution:
+#' Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
 #'
-#' \ifelse{latex}{\deqn{$theta$ | y \sim \text{Beta}($alpha$_0 + y, $beta$_0 + n - y)}}{
-#' \ifelse{html}{\figure{posterior_beta.png}{options: width="300" alt="Beta posterior"}}{$theta$ | y ~ Beta($alpha$_0 + y, $beta$_0 + n - y)}}
+#' \deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
 #'
-#' For hierarchical modeling, pathogen-level effects (e.g., differences in resistance 
-#' patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. 
-#' This hierarchical structure ensures partial pooling of estimates across groups, 
-#' improving stability in strata with small sample sizes. The model is implemented using 
-#' Hamiltonian Monte Carlo (HMC) sampling.
+#' For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
 #'
-#' Stratified results are provided based on covariates such as age, sex, and clinical 
-#' complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities). 
-#' For example, posterior odds ratios (ORs) are derived to quantify the effect of these 
-#' covariates on coverage probabilities:
+#' Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using `dplyr`'s [group_by()] as a pre-processing step before running [wisca()]. In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
 #'
-#' \ifelse{latex}{\deqn{\text{OR}_{\text{covariate}} = \frac{\exp($beta$_{\text{covariate}})}{\exp($beta$_0)}}}{
-#' \ifelse{html}{\figure{odds_ratio.png}{options: width="300" alt="Odds ratio formula"}}{OR_covariate = exp(beta_covariate) / exp(beta_0)}}
+#' \deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
 #'
 #' By combining empirical data with prior knowledge, WISCA overcomes the limitations 
 #' of traditional combination antibiograms, offering disease-specific, patient-stratified 
@@ -249,6 +227,7 @@
 #' * Klinker KP *et al.* (2021). **Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms**. *Therapeutic Advances in Infectious Disease*, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
 #' * Barbieri E *et al.* (2021). **Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach** *Antimicrobial Resistance & Infection Control* May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
 #' * **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
+#' @author Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
 #' @rdname antibiogram
 #' @name antibiogram
 #' @export
@@ -331,7 +310,8 @@
 #'
 #' ureido <- antibiogram(example_isolates,
 #'   antibiotics = ureidopenicillins(),
-#'   ab_transform = "name"
+#'   ab_transform = "name",
+#'   wisca = TRUE
 #' )
 #'
 #' # in an Rmd file, you would just need to return `ureido` in a chunk,

R/mo_matching_score.R

@@ -38,9 +38,7 @@
 #' @section Matching Score for Microorganisms:
 #' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m}, is calculated as:
 #'
-#' \ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
-#'
-#' \ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
+#' \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
 #'
 #' where:
 #'

_pkgdown.yml

@@ -32,6 +32,11 @@ url: "https://msberends.github.io/AMR/"
 
 template:
   bootstrap: 5
+  includes: # support for mathematical formulas, from https://github.com/r-lib/pkgdown/issues/2704#issuecomment-2307055568
+    in_header: |
+      <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous">
+      <script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script>
+      <script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script>
   bootswatch: "flatly"
   assets: "pkgdown/logos" # use logos in this folder
   bslib:

data-raw/_generate_python_wrapper.sh

@@ -65,7 +65,6 @@ utils = importr('utils')
 base.options(warn = -1)
 
 # Override R library paths globally for the session
-robjects.r(f'.Library <- "{r_lib_path}"')  # Replace default library
 robjects.r(f'.Library.site <- "{r_lib_path}"')  # Replace site-specific library
 base._libPaths(r_lib_path)  # Override .libPaths() as well
 

data-raw/antibiograms.Rmd

@@ -1,54 +0,0 @@
----
-title: "Generating antibiograms with the AMR package"
-author: "AMR package developers"
-date: "`r Sys.Date()`"
-output: pdf_document
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE, message = FALSE)
-library(AMR)
-```
-
-This is an example R Markdown file to show the use of `antibiogram()` of the AMR package.
-
-For starters, this is what our `example_isolates` data set looks like:
-
-```{r}
-example_isolates
-```
-
-
-### Traditional Antibiogram
-
-```{r trad}
-antibiogram(example_isolates,
-            antibiotics = c(aminoglycosides(), carbapenems()))
-```
-
-### Combined Antibiogram
-
-```{r comb}
-antibiogram(example_isolates,
-            antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
-```
-
-### Syndromic Antibiogram
-
-```{r synd}
-antibiogram(example_isolates,
-             antibiotics = c(aminoglycosides(), carbapenems()),
-             syndromic_group = "ward")
-```
-
-### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
-
-```{r wisca}
-antibiogram(example_isolates,
-            antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
-            mo_transform = "gramstain",
-            minimum = 10, # this should be >= 30, but now just as example
-            syndromic_group = ifelse(example_isolates$age >= 65 &
-                                       example_isolates$gender == "M",
-                                     "WISCA Group 1", "WISCA Group 2"))
-```

data-raw/gpt_training_text_v2.1.1.9133.txt

@@ -1854,48 +1854,26 @@ You can also use functions from specific 'table reporting' packages to transform
 }
 \section{Why Use WISCA?}{
 
-WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for
-Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability
-of adequate empirical antimicrobial regimen coverage for specific infection syndromes.
-This method leverages a Bayesian hierarchical logistic regression framework with random
-effects for pathogens and regimens, enabling robust estimates in the presence of sparse
-data.
 
-The Bayesian model assumes conjugate priors for parameter estimation. For example, the
-coverage probability \ifelse{latex}{\deqn{$theta$}}{$theta$} for a given antimicrobial regimen
-is modeled using a Beta distribution as a prior:
+WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.
 
-\ifelse{latex}{\deqn{$theta$ \sim \text{Beta}($alpha$_0, $beta$_0)}}{
-\ifelse{html}{\figure{beta_prior.png}{options: width="300" alt="Beta prior"}}{$theta$ ~ Beta($alpha$_0, $beta$_0)}}
+The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
 
-where \eqn{$alpha$_0} and \eqn{$beta$_0} represent prior successes and failures, respectively,
-informed by expert knowledge or weakly informative priors (e.g., \eqn{$alpha$_0 = 1, $beta$_0 = 1}).
+\deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
 
-The likelihood function is constructed based on observed data, where the number of covered
-cases for a regimen follows a binomial distribution:
+where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
 
-\ifelse{latex}{\deqn{y \sim \text{Binomial}(n, $theta$)}}{
-\ifelse{html}{\figure{binomial_likelihood.png}{options: width="300" alt="Binomial likelihood"}}{y ~ Binomial(n, $theta$)}}
+\deqn{y \sim \text{Binomial}(n, \theta)}
 
-Posterior parameter estimates are obtained by combining the prior and likelihood using
-Bayes' theorem. The posterior distribution of \eqn{$theta$} is also a Beta distribution:
+Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
 
-\ifelse{latex}{\deqn{$theta$ | y \sim \text{Beta}($alpha$_0 + y, $beta$_0 + n - y)}}{
-\ifelse{html}{\figure{posterior_beta.png}{options: width="300" alt="Beta posterior"}}{$theta$ | y ~ Beta($alpha$_0 + y, $beta$_0 + n - y)}}
+\deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
 
-For hierarchical modeling, pathogen-level effects (e.g., differences in resistance
-patterns) and regimen-level effects are modelled using Gaussian priors on log-odds.
-This hierarchical structure ensures partial pooling of estimates across groups,
-improving stability in strata with small sample sizes. The model is implemented using
-Hamiltonian Monte Carlo (HMC) sampling.
+For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
 
-Stratified results are provided based on covariates such as age, sex, and clinical
-complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities).
-For example, posterior odds ratios (ORs) are derived to quantify the effect of these
-covariates on coverage probabilities:
+Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using \code{dplyr}'s \code{\link[=group_by]{group_by()}} as a pre-processing step before running \code{\link[=wisca]{wisca()}}. In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
 
-\ifelse{latex}{\deqn{\text{OR}_{\text{covariate}} = \frac{\exp($beta$_{\text{covariate}})}{\exp($beta$_0)}}}{
-\ifelse{html}{\figure{odds_ratio.png}{options: width="300" alt="Odds ratio formula"}}{OR_covariate = exp(beta_covariate) / exp(beta_0)}}
+\deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
 
 By combining empirical data with prior knowledge, WISCA overcomes the limitations
 of traditional combination antibiograms, offering disease-specific, patient-stratified
@@ -1982,7 +1960,8 @@ antibiogram(example_isolates,
 
 ureido <- antibiogram(example_isolates,
   antibiotics = ureidopenicillins(),
-  ab_transform = "name"
+  ab_transform = "name",
+  wisca = TRUE
 )
 
 # in an Rmd file, you would just need to return `ureido` in a chunk,
@@ -2016,6 +1995,9 @@ plot(ab1)
 plot(ab2)
 }
 }
+\author{
+Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
+}
 
 
 
@@ -3099,9 +3081,7 @@ This is based on:
 
 With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, is calculated as:
 
-\ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
-
-\ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
+\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
 
 where:
 \itemize{
@@ -6627,9 +6607,7 @@ Later, the work of Bartlett A \emph{et al.} about bacterial pathogens infecting
 
 With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, is calculated as:
 
-\ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
-
-\ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
+\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
 
 where:
 \itemize{

man/antibiogram.Rd

@@ -232,48 +232,26 @@ You can also use functions from specific 'table reporting' packages to transform
 }
 \section{Why Use WISCA?}{
 
-WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for
-Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability
-of adequate empirical antimicrobial regimen coverage for specific infection syndromes.
-This method leverages a Bayesian hierarchical logistic regression framework with random
-effects for pathogens and regimens, enabling robust estimates in the presence of sparse
-data.
 
-The Bayesian model assumes conjugate priors for parameter estimation. For example, the
-coverage probability \ifelse{latex}{\deqn{$theta$}}{$theta$} for a given antimicrobial regimen
-is modeled using a Beta distribution as a prior:
+WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.
 
-\ifelse{latex}{\deqn{$theta$ \sim \text{Beta}($alpha$_0, $beta$_0)}}{
-\ifelse{html}{\figure{beta_prior.png}{options: width="300" alt="Beta prior"}}{$theta$ ~ Beta($alpha$_0, $beta$_0)}}
+The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
 
-where \eqn{$alpha$_0} and \eqn{$beta$_0} represent prior successes and failures, respectively,
-informed by expert knowledge or weakly informative priors (e.g., \eqn{$alpha$_0 = 1, $beta$_0 = 1}).
+\deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
 
-The likelihood function is constructed based on observed data, where the number of covered
-cases for a regimen follows a binomial distribution:
+where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
 
-\ifelse{latex}{\deqn{y \sim \text{Binomial}(n, $theta$)}}{
-\ifelse{html}{\figure{binomial_likelihood.png}{options: width="300" alt="Binomial likelihood"}}{y ~ Binomial(n, $theta$)}}
+\deqn{y \sim \text{Binomial}(n, \theta)}
 
-Posterior parameter estimates are obtained by combining the prior and likelihood using
-Bayes' theorem. The posterior distribution of \eqn{$theta$} is also a Beta distribution:
+Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
 
-\ifelse{latex}{\deqn{$theta$ | y \sim \text{Beta}($alpha$_0 + y, $beta$_0 + n - y)}}{
-\ifelse{html}{\figure{posterior_beta.png}{options: width="300" alt="Beta posterior"}}{$theta$ | y ~ Beta($alpha$_0 + y, $beta$_0 + n - y)}}
+\deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
 
-For hierarchical modeling, pathogen-level effects (e.g., differences in resistance
-patterns) and regimen-level effects are modelled using Gaussian priors on log-odds.
-This hierarchical structure ensures partial pooling of estimates across groups,
-improving stability in strata with small sample sizes. The model is implemented using
-Hamiltonian Monte Carlo (HMC) sampling.
+For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
 
-Stratified results are provided based on covariates such as age, sex, and clinical
-complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities).
-For example, posterior odds ratios (ORs) are derived to quantify the effect of these
-covariates on coverage probabilities:
+Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using \code{dplyr}'s \code{\link[=group_by]{group_by()}} as a pre-processing step before running \code{\link[=wisca]{wisca()}}. In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
 
-\ifelse{latex}{\deqn{\text{OR}_{\text{covariate}} = \frac{\exp($beta$_{\text{covariate}})}{\exp($beta$_0)}}}{
-\ifelse{html}{\figure{odds_ratio.png}{options: width="300" alt="Odds ratio formula"}}{OR_covariate = exp(beta_covariate) / exp(beta_0)}}
+\deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
 
 By combining empirical data with prior knowledge, WISCA overcomes the limitations
 of traditional combination antibiograms, offering disease-specific, patient-stratified
@@ -360,7 +338,8 @@ antibiogram(example_isolates,
 
 ureido <- antibiogram(example_isolates,
   antibiotics = ureidopenicillins(),
-  ab_transform = "name"
+  ab_transform = "name",
+  wisca = TRUE
 )
 
 # in an Rmd file, you would just need to return `ureido` in a chunk,
@@ -394,3 +373,6 @@ plot(ab1)
 plot(ab2)
 }
 }
+\author{
+Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
+}

man/as.mo.Rd

@@ -169,9 +169,7 @@ This is based on:
 
 With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, is calculated as:
 
-\ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
-
-\ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
+\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
 
 where:
 \itemize{

man/mo_matching_score.Rd

@@ -23,9 +23,7 @@ Later, the work of Bartlett A \emph{et al.} about bacterial pathogens infecting
 
 With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, is calculated as:
 
-\ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
-
-\ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
+\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
 
 where:
 \itemize{

tests/testthat.R

@@ -37,9 +37,6 @@ expect_inherits <- function(x, y, ...) {
                                   ", required is class ", paste0(y, collapse = "/")))
 }
 expect_stdout <- expect_output
-if (getRversion() < "4.0.0") {
-  deparse1 <- AMR:::deparse1
-}
 
 # start unit tests
 test_check("AMR")