Add code at beginning of pages to homogenise content and improve visu…

…al appearance of pages

advanced_monty.qmd

@@ -1,5 +1,10 @@
 # Advanced monty
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 ## Observers
 
 ## Working with other packages

composability.qmd

@@ -1,5 +1,10 @@
 # Composability {#sec-monty-combine}
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 ```{r}
 library(monty)
 ```

model.qmd

@@ -1,5 +1,10 @@
 # About monty models {#sec-monty-models}
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 ## SimCity vs Statistical models
 
 ## Normalised vs unormalised densities

monty.qmd

@@ -1,5 +1,10 @@
 # Getting started with monty
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 The `monty` R package is designed for modular work with statistical distributions, leveraging Monte Carlo methods for sampling. It enables the construction of increasingly complex models through four approaches:
 
 1. Simple R code,

montyDSL.qmd

@@ -1,5 +1,10 @@
 # The monty DSL {#sec-monty-dsl}
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 The `monty` DSL provides a more intuitive way to define statistical models with `monty`. It is currently relatively basic and focus on providing support for defining priors in Bayesian models. It fully support differentiability allowing to use gradient based samplers on these models.
 
 `monty` includes a simple probabilistic domain-specific language (DSL) that is inspired by `stan` and [Statistical Rethinking](https://xcelab.net/rm/).  It is designed to make some tasks a bit easier, particularly when defining priors for your model.  We expect that this DSL is not sufficiently advanced to represent most interesting models but it may get more clever and flexible in the future.  In particular we do not expect the DSL to be useful in writing likelihood functions for comparison to data; we expect that if your model is simple enough for this you would be better off using `stan` or some similarly flexible system.

runners.qmd

@@ -1,3 +1,8 @@
 # Runners
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 One of the focus of `monty` is tailoring implementation to optimise usage of available hardware while ensuring reproducible results accross platforms.

samplers.qmd

@@ -1,5 +1,10 @@
 # Samplers and inference
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 This vignette will describe the samplers available in monty.
 
 ```{r}