Remove R vs Julia transformation recommendations and fix KDE plot

- Removed statements recommending R's built-in functions over Julia wrappers
  from both transformations.Rmd and forecastbaselines.Rmd
- Fixed KDE vs Marginal plot by switching from overlapping histograms to
  density plots, which better shows the smoothing effect and avoids the
  "blue rectangle" visualization issue

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

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

Author: sbfnk

vignettes/forecast-models.Rmd

@@ -240,16 +240,14 @@ fc_marginal <- forecast(marginal, interval_method = NoInterval(), horizon = 1:10
 fc_kde <- forecast(kde, interval_method = NoInterval(), horizon = 1:100)
 
 # KDE produces smoother samples
-hist(fc_marginal$mean,
-  breaks = 20, main = "Marginal vs KDE",
-  col = rgb(1, 0, 0, 0.5)
-)
-hist(fc_kde$mean,
-  breaks = 20, add = TRUE,
-  col = rgb(0, 0, 1, 0.5)
+plot(density(fc_marginal$mean),
+  main = "Marginal vs KDE",
+  col = "red", lwd = 2,
+  xlab = "Forecast Value", ylab = "Density"
 )
+lines(density(fc_kde$mean), col = "blue", lwd = 2)
 legend("topright", c("Marginal", "KDE"),
-  fill = c(rgb(1, 0, 0, 0.5), rgb(0, 0, 1, 0.5))
+  col = c("red", "blue"), lwd = 2
 )
 ```
 

vignettes/forecastbaselines.Rmd

@@ -359,8 +359,7 @@ legend("topleft",
 # Data Transformations
 
 For data with non-constant variance or non-negativity constraints,
-use transformations. **We recommend using R's built-in functions**
-rather than the Julia transformation wrappers.
+use transformations.
 
 ```{r transformations}
 # Example: exponential growth (log transform stabilizes variance)

vignettes/transformations.Rmd

@@ -23,8 +23,6 @@ When working with time series data, transformations are often needed to:
 - **Ensure non-negativity** (e.g., for count data)
 - **Improve model fit** (e.g., linearize relationships)
 
-While forecastbaselines includes some Julia-based transformation functions, **we strongly recommend using R's built-in transformation functions** for better reliability, flexibility, and performance.
-
 # Why Use R Transformations?
 
 **Advantages:**