Improve documentation readability and add related packages

- Add explicit library(scoringutils) call in README example
- Change model names from bold to italic throughout documentation
- Remove excessive bold formatting from features, prerequisites, and section headers
- Add Related Packages section acknowledging fable, forecast, prophet, and modeltime

This reduces visual clutter and improves accessibility while maintaining clear structure.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: sbfnk

README.Rmd

@@ -26,18 +26,18 @@ An R package providing access to 10 baseline forecasting models from the Julia F
 
 ## Features
 
-- **10 Forecasting Models**: From simple baselines (Constant, Marginal) to advanced time series models (ARMA, ETS, STL)
-- **Probabilistic Forecasting**: Multiple methods for prediction intervals (empirical, parametric, model-based)
-- **Comprehensive Scoring**: Compatible with scoringutils for all standard forecast evaluation metrics
-- **Data Transformations**: Log, power, Box-Cox transformations with automatic back-transformation
+- 10 Forecasting Models: From simple baselines (Constant, Marginal) to advanced time series models (ARMA, ETS, STL)
+- Probabilistic Forecasting: Multiple methods for prediction intervals (empirical, parametric, model-based)
+- Comprehensive Scoring: Compatible with scoringutils for all standard forecast evaluation metrics
+- Data Transformations: Log, power, Box-Cox transformations with automatic back-transformation
 
 ## Installation
 
 ### Prerequisites
 
-1. **Julia** (>= 1.9): Download from [julialang.org](https://julialang.org/downloads/)
-2. **R** (>= 3.5.0)
-3. **JuliaCall R package**: `install.packages("JuliaCall")`
+1. Julia (>= 1.9): Download from [julialang.org](https://julialang.org/downloads/)
+2. R (>= 3.5.0)
+3. JuliaCall R package: `install.packages("JuliaCall")`
 
 ### Installing forecastbaselines
 
@@ -62,6 +62,8 @@ This only needs to be done once per R session.
 ## Quick Example
 
 ```{r example}
+library(scoringutils) # for scoring
+
 # Your time series data
 data <- c(1.2, 2.3, 3.1, 2.8, 3.5, 4.2, 3.9, 4.5, 4.1, 4.8)
 
@@ -93,20 +95,20 @@ scores_summary[, c("model", "ae_point", "se_point")]
 ## Available Models
 
 ### Simple Baseline Models
-- **ConstantModel**: Naive forecast using last observed value
-- **MarginalModel**: Forecasts based on empirical marginal distribution
-- **KDEModel**: Kernel density estimation
+- *ConstantModel*: Naive forecast using last observed value
+- *MarginalModel*: Forecasts based on empirical marginal distribution
+- *KDEModel*: Kernel density estimation
 
 ### Seasonal/Trend Models
-- **LSDModel**: Last Similar Dates method (seasonal patterns)
-- **OLSModel**: Ordinary least squares with polynomial trends
-- **IDSModel**: Increase-Decrease-Stable trend detection
-- **STLModel**: Seasonal-Trend decomposition using Loess
+- *LSDModel*: Last Similar Dates method (seasonal patterns)
+- *OLSModel*: Ordinary least squares with polynomial trends
+- *IDSModel*: Increase-Decrease-Stable trend detection
+- *STLModel*: Seasonal-Trend decomposition using Loess
 
 ### Advanced Time Series Models
-- **ARMAModel**: Autoregressive Moving Average
-- **INARCHModel**: Integer-valued ARCH for count data
-- **ETSModel**: Error-Trend-Season exponential smoothing (all 30 variants)
+- *ARMAModel*: Autoregressive Moving Average
+- *INARCHModel*: Integer-valued ARCH for count data
+- *ETSModel*: Error-Trend-Season exponential smoothing (all 30 variants)
 
 ## Documentation
 
@@ -116,11 +118,20 @@ For detailed guides and examples:
 - `vignette("forecast-models")` - Detailed guide to all 10 models
 - `vignette("transformations")` - Working with data transformations
 
+## Related Packages
+
+For comprehensive time series forecasting in R, consider:
+
+- [fable](https://fable.tidyverts.org/) - A complete forecasting framework in the [tidyverts](https://tidyverts.org/) ecosystem
+- [forecast](https://pkg.robjhyndman.com/forecast/) - The classic R forecasting package with auto.arima, ets, and many other methods
+- [prophet](https://facebook.github.io/prophet/) - Facebook's forecasting package for time series with strong seasonal patterns
+- [modeltime](https://business-science.github.io/modeltime/) - A tidymodels framework for time series forecasting
+
 ## Citation
 
 If you use this package in your research, please cite the software and the associated preprint:
 
-**Software:**
+Software:
 ```
 @software{forecastbaselinesr,
   title = {forecastbaselines: R Interface to ForecastBaselines.jl},
@@ -130,7 +141,7 @@ If you use this package in your research, please cite the software and the assoc
 }
 ```
 
-**Preprint:**
+Preprint:
 ```
 @article{stapper2025baseline,
   title = {Mind the Baseline: The Hidden Impact of Reference Model Selection on Forecast Assessment},