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[Bug]: lists are wrapped #103

@topepo

Description

@topepo

Component

Formatter

Panache Version

2.29.0

Operating System

macOS

Description

My .panache.toml includes:

# Formatting style
[style]
wrap = "preserve"

using panache format *.qmd resulting in wrapping list items.

Input Document

1. **Simple model**: Convert each of the `r length(levels(forested_train$county))` counties to binary indicators and drop any predictors with zero-variance. 
 2. **Normalization model**: Begin with the simple model and add a normalization step that applies the ORD transformation to all numeric predictors. 
 3. **Encoding model**:  Build on the normalization model by replacing the county dummy indicators with effect encoding.
 4. **Interaction model**:  extend the encoding by including interaction terms. 
 5. **Spline model**:  Enhance the interaction model further with ten natural spline basis functions for a set of predictors.

Expected Output

I expected no change: 


 1. **Simple model**: Convert each of the `r length(levels(forested_train$county))` counties to binary indicators and drop any predictors with zero-variance. 
 2. **Normalization model**: Begin with the simple model and add a normalization step that applies the ORD transformation to all numeric predictors. 
 3. **Encoding model**:  Build on the normalization model by replacing the county dummy indicators with effect encoding.
 4. **Interaction model**:  extend the encoding by including interaction terms. 
 5. **Spline model**:  Enhance the interaction model further with ten natural spline basis functions for a set of predictors.

Actual Output

1. **Simple model**: Convert each of the
   `r length(levels(forested_train$county))` counties to binary indicators and
   drop any predictors with zero-variance.
2. **Normalization model**: Begin with the simple model and add a normalization
   step that applies the ORD transformation to all numeric predictors.
3. **Encoding model**: Build on the normalization model by replacing the county
   dummy indicators with effect encoding.
4. **Interaction model**: extend the encoding by including interaction terms.
5. **Spline model**: Enhance the interaction model further with ten natural
   spline basis functions for a set of predictors.

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