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(v3.0.0.9026) fix tidymodels doc

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dr. M.S. (Matthijs) Berends
2025-09-04 15:03:28 +02:00
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DESCRIPTION
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Package: AMR Package: AMR
Version: 3.0.0.9025 Version: 3.0.0.9026
Date: 2025-09-04 Date: 2025-09-04
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)

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NEWS.md
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# AMR 3.0.0.9025 # AMR 3.0.0.9026
This is a bugfix release following the release of v3.0.0 in June 2025. This is a bugfix release following the release of v3.0.0 in June 2025.

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vignettes/AMR_with_tidymodels.Rmd
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@@ -221,6 +221,12 @@ In this second example, we demonstrate how to use `<mic>` columns directly in `t
This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703) to model the presence of extended-spectrum beta-lactamases (ESBL). This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703) to model the presence of extended-spectrum beta-lactamases (ESBL).
> NOTE: THIS EXAMPLE WILL BE AVAILABLE IN A NEXT VERSION (#TODO)
>
> The new AMR package version will contain new tidymodels selectors such as `step_mic_log2()`.
<!--
### **Objective** ### **Objective**
Our goal is to: Our goal is to:
@@ -347,12 +353,16 @@ ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
theme_minimal() theme_minimal()
``` ```
<!--
### **Conclusion** ### **Conclusion**
In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` transformation converts ordered MICs to log2-transformed numerics, improving compatibility with classification models. In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` transformation converts ordered MICs to log2-transformed numerics, improving compatibility with classification models.
This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data. This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data.
-->
--- ---