(v2.1.1.9133) update math formulae

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{