(v2.1.1.9239) documentation

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9237
-Date: 2025-04-13
+Version: 2.1.1.9239
+Date: 2025-04-14
 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

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9237
+# AMR 2.1.1.9239
 
 *(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://amr-for-r.org/#get-this-package).)*
 
@@ -106,10 +106,10 @@ This package now supports not only tools for AMR data analysis in clinical setti
 * Added console colours support of `sir` class for Positron
 
 ## Other
-* New website domain: <https://amr-for-r.org>! The old links (all based on <http://amr-for-r.org>) will remain to work.
+* New website domain: <https://amr-for-r.org>! The old domain (<https://msberends.github.io/AMR>) will remain to work.
 * Added Dr. Larisse Bolton and Aislinn Cook as contributors for their fantastic implementation of WISCA in a mathematically solid way
 * Added Matthew Saab, Dr. Jordan Stull, and Prof. Javier Sanchez as contributors for their tremendous input on veterinary breakpoints and interpretations
-* Added Prof. Kat Holt, Dr. Jane Hawkey, and Dr. Natacha Couto as contributors for their many suggestions, ideas and bugfixes
+* Added Prof. Kathryn Holt, Dr. Jane Hawkey, and Dr. Natacha Couto as contributors for their many suggestions, ideas and bugfixes
 * Greatly improved `vctrs` integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as `dplyr`'s `bind_rows()`, `rowwise()` and `c_across()` are now supported for e.g. columns of class `mic`. Despite this, this `AMR` package is still zero-dependent on any other package, including `dplyr` and `vctrs`.
 * Greatly updated and expanded documentation
 * Stopped support for SAS (`.xpt`) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.

data-raw/gpt_training_text_v2.1.1.9239.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 2.1.1.9237. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9239. Remember this whenever someone asks which AMR package version you’re at.
 
 Below are the contents of the NAMESPACE file, the index.md file, and all the man/*.Rd files (documentation) in the package. Every file content is split using 100 hypens.
 ----------------------------------------------------------------------------------------------------
@@ -9325,7 +9325,7 @@ logistic_model
 
 #### 3. Building the Workflow
 
-We bundle the recipe and model together into a `workflow`, which organises the entire modeling process.
+We bundle the recipe and model together into a `workflow`, which organises the entire modelling process.
 
 ```{r}
 # Combine the recipe and model into a workflow
@@ -9455,7 +9455,7 @@ data_time
 
 ### **Defining the Workflow**
 
-We now define the modeling workflow, which consists of a preprocessing step, a model specification, and the fitting process.
+We now define the modelling workflow, which consists of a preprocessing step, a model specification, and the fitting process.
 
 #### 1. Preprocessing with a Recipe
 
@@ -10209,6 +10209,7 @@ microorganisms %>%
   print_df()
 ```
 
+----
 
 ## `antimicrobials`: Antibiotic and Antifungal Drugs
 
@@ -10228,6 +10229,7 @@ antimicrobials %>%
   print_df()
 ```
 
+----
 
 ## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR
 
@@ -10246,6 +10248,7 @@ clinical_breakpoints %>%
   print_df()
 ```
 
+----
 
 ## `microorganisms.groups`: Species Groups and Microbiological Complexes
 
@@ -10262,6 +10265,7 @@ microorganisms.groups %>%
   print_df()
 ```
 
+----
 
 ## `intrinsic_resistant`: Intrinsic Bacterial Resistance
 
@@ -10286,6 +10290,7 @@ intrinsic_resistant %>%
   print_df(rows = Inf)
 ```
 
+----
 
 ## `dosage`: Dosage Guidelines from EUCAST
 
@@ -10302,6 +10307,7 @@ dosage %>%
   print_df()
 ```
 
+----
 
 ## `example_isolates`: Example Data for Practice
 
@@ -10318,6 +10324,8 @@ example_isolates %>%
   print_df()
 ```
 
+----
+
 ## `example_isolates_unclean`: Example Data for Practice
 
 `r structure_txt(example_isolates_unclean)`
@@ -10333,6 +10341,7 @@ example_isolates_unclean %>%
   print_df()
 ```
 
+----
 
 ## `microorganisms.codes`: Common Laboratory Codes
 
@@ -10349,6 +10358,7 @@ microorganisms.codes %>%
   print_df()
 ```
 
+----
 
 ## `antivirals`: Antiviral Drugs
 

pkgdown/extra.css

@@ -129,6 +129,8 @@ input[type="search"] {
 }
 .template-home .endorse_img {
   width: 150px;
+  margin-left: 10px;
+  margin-bottom: 10px;
 }
 blockquote {
   font-style: italic;

vignettes/AMR_with_tidymodels.Rmd

@@ -123,7 +123,7 @@ logistic_model
 
 #### 3. Building the Workflow
 
-We bundle the recipe and model together into a `workflow`, which organises the entire modeling process.
+We bundle the recipe and model together into a `workflow`, which organises the entire modelling process.
 
 ```{r}
 # Combine the recipe and model into a workflow
@@ -253,7 +253,7 @@ data_time
 
 ### **Defining the Workflow**
 
-We now define the modeling workflow, which consists of a preprocessing step, a model specification, and the fitting process.
+We now define the modelling workflow, which consists of a preprocessing step, a model specification, and the fitting process.
 
 #### 1. Preprocessing with a Recipe
 

vignettes/datasets.Rmd

@@ -144,6 +144,7 @@ microorganisms %>%
   print_df()
 ```
 
+----
 
 ## `antimicrobials`: Antibiotic and Antifungal Drugs
 
@@ -163,6 +164,7 @@ antimicrobials %>%
   print_df()
 ```
 
+----
 
 ## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR
 
@@ -181,6 +183,7 @@ clinical_breakpoints %>%
   print_df()
 ```
 
+----
 
 ## `microorganisms.groups`: Species Groups and Microbiological Complexes
 
@@ -197,6 +200,7 @@ microorganisms.groups %>%
   print_df()
 ```
 
+----
 
 ## `intrinsic_resistant`: Intrinsic Bacterial Resistance
 
@@ -221,6 +225,7 @@ intrinsic_resistant %>%
   print_df(rows = Inf)
 ```
 
+----
 
 ## `dosage`: Dosage Guidelines from EUCAST
 
@@ -237,6 +242,7 @@ dosage %>%
   print_df()
 ```
 
+----
 
 ## `example_isolates`: Example Data for Practice
 
@@ -253,6 +259,8 @@ example_isolates %>%
   print_df()
 ```
 
+----
+
 ## `example_isolates_unclean`: Example Data for Practice
 
 `r structure_txt(example_isolates_unclean)`
@@ -268,6 +276,7 @@ example_isolates_unclean %>%
   print_df()
 ```
 
+----
 
 ## `microorganisms.codes`: Common Laboratory Codes
 
@@ -284,6 +293,7 @@ microorganisms.codes %>%
   print_df()
 ```
 
+----
 
 ## `antivirals`: Antiviral Drugs