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(v3.0.1.9007) fix vignette

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2025-12-22 09:34:58 +01:00
parent f6e28ac95c
commit a5c6aa9fa8
6 changed files with 7 additions and 7 deletions
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6
.github/ISSUE_TEMPLATE/1-bug-report.yml vendored
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@@ -22,9 +22,9 @@ body:
label: Minimal Reproducible Example (optional) label: Minimal Reproducible Example (optional)
description: Please include a short R code snippet that reproduces the problem, if possible. description: Please include a short R code snippet that reproduces the problem, if possible.
placeholder: placeholder:
e.g.\n\n e.g.
```r<br> ```r
ab_name("amoxicillin/clavulanic acid", language = "es")\n ab_name("amoxicillin/clavulanic acid", language = "es")
``` ```
validations: validations:
required: false required: false

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DESCRIPTION
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@@ -1,6 +1,6 @@
Package: AMR Package: AMR
Version: 3.0.1.9006 Version: 3.0.1.9007
Date: 2025-12-21 Date: 2025-12-22
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)
data analysis and to work with microbial and antimicrobial properties by data analysis and to work with microbial and antimicrobial properties by

2
NEWS.md
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@@ -1,4 +1,4 @@
# AMR 3.0.1.9006 # AMR 3.0.1.9007
### New ### New
* Integration with the **tidymodels** framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes` * Integration with the **tidymodels** framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes`

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vignettes/AMR_with_tidymodels.Rmd
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@@ -315,7 +315,7 @@ fitted <- fit(workflow_model, training_data)
# Generate predictions # Generate predictions
predictions <- predict(fitted, testing_data) %>% predictions <- predict(fitted, testing_data) %>%
bind_cols(predict(fitted, out_testing, type = "prob")) %>% # add probabilities bind_cols(predict(fitted, testing_data, type = "prob")) %>% # add probabilities
bind_cols(testing_data) bind_cols(testing_data)
# Evaluate model performance # Evaluate model performance