Merge pull request #16 from mrc-ide/fix-code-bg

Fix code background

common.R

@@ -17,5 +17,5 @@ r_output <- function(x) {
 }
 
 plain_output <- function(x) {
-  lang_output(x, "")
+  lang_output(x, "md") # not great, but at least renders nicely
 }

differentiability.qmd

@@ -1,3 +1,8 @@
 # Differentiability
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 Random walk suppression and other uses

installation.qmd

@@ -1,5 +1,10 @@
 # Installation
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 Package installation is hard, let's go shopping.
 
 ## Packages

monty.qmd

@@ -1,5 +1,10 @@
 # Monty
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 Before showing how to fit odin models to data, we'll focus for a bit on monty itself.  If you are anxious to get on and fit the model from @sec-data-filter, you might skip ahead to @sec-inference, where we resume this.
 
 There are a bunch of things we want to cover in this chapter so likely it will be split into a few:

odin.qmd

@@ -13,7 +13,15 @@ library(odin2)
 
 ## A simple example {#sec-odin-sir}
 
-Here is a small system of equations for an "SIR" (Susceptible-Infected-Recovered) model, implemented as a set of ordinary differential equations:
+Here is a small system of differential equations for an "SIR" (Susceptible-Infected-Recovered) model:
+
+\begin{gather*}
+\frac{dS}{dt} = -\beta S \frac{I}{N}\\
+\frac{dI}{dt} = \beta S \frac{I}{N} - \gamma I\\
+\frac{dR}{dt} = \gamma I
+\end{gather*}
+
+And here is an implementation of these equations in `odin`:
 
 ```{r}
 sir <- odin({

references.qmd

@@ -1,4 +1,9 @@
 # References {.unnumbered}
 
+```{r}
+#| include: false
+source("common.R")
+```
+
 ::: {#refs}
 :::

stochasticity.qmd

@@ -61,6 +61,8 @@ sir <- odin({
 
 This is a discrete-time model (using `update()` and not `deriv()`) as stochastic models must run in discrete time.  We use `dt` to scale the rates, and adjusting `dt` will change the way that stochasticity affects the dynamics of the system.
 
+The call to `Binomial()` samples from a binomial distribution, returning the number of successes from `S` (or `I`) draws, each with probability `p_SI` (or `p_IR`).
+
 ```{r}
 sir
 ```