move doc for register_primitives to a separate page

docs/make.jl

@@ -14,6 +14,7 @@ makedocs(;
             "General" => "api.md",
             "Functions" => "functions.md",
             "Distributions" => "distributions.md",
+            "User-defined Functions and Distributions" => "user_defined_functions.md"
         ],
         "Plotting" => "graph_plotting.md",
         "R Interface" => "R_interface.md",

docs/src/example.md

@@ -1,6 +1,5 @@
 # Example: Logistic Regression with Random Effects
 
-## Example: Logistic Regression with Random Effects
 We will use the [Seeds](https://chjackson.github.io/openbugsdoc/Examples/Seeds.html) model for demonstration. 
 This example concerns the proportion of seeds that germinated on each of 21 plates. Here, we transform the data into a `NamedTuple`:
 
@@ -107,32 +106,6 @@ model{
 By default, `@bugs` will translate R-style variable names like `a.b.c` to `a_b_c`, user can pass `false` as the second argument to disable this. 
 We still encourage users to write new programs using the Julia-native syntax, because of better debuggability and perks like syntax highlighting. 
 
-### Using Self-defined Functions and Distributions
-Users can register their own functions and distributions with macros. However, note that any functions used must be _pure_ mathematical functions, i.e., side-effect free.
-
-```julia
-julia> # Should be restricted to pure functions that do simple operations
-@register_primitive function f(x)
-    return x + 1
-end
-
-julia> JuliaBUGS.f(2)
-3
-```
-
-Users can also `introduce` a function into `JuliaBUGS`, by 
-
-```julia
-julia> f(x) = x + 1
-
-julia> @register_primitive(f)
-
-julia> JuliaBUGS.f(1)
-2
-```
-
-After registering the function or distributions, they can be used just like any other functions or distributions provided by BUGS.
-
 ## Compilation
 
 For now, the `compile` function will create a `BUGSModel`, which implements [`LogDensityProblems.jl`](https://github.com/tpapp/LogDensityProblems.jl) interface.

docs/src/index.md

@@ -4,7 +4,7 @@ The package aims to support modelling and inference for probabilistic programs w
 
 This project is still in its early stage, with many key components needing to be completed. 
 
-Please refer to the [Github project page](https://github.com/TuringLang/SymbolicPPL.jl) for usage information and a complete example.
+Please refer to the [example](https://turinglang.org/JuliaBUGS.jl/stable/example/) for usage information and a complete example.
 
 ## What is BUGS?
 

docs/src/user_defined_functions.md

@@ -0,0 +1,29 @@
+# Define and Use Your Own Functions and Distributions
+
+For now, out of the box, JuliaBUGS only allows functions and distributions defined in `BUGSPrimitives` to be used in the model.
+With the `@register_primitive` macro, users can register their own functions and distributions with JuliaBUGS. It is important to ensure that any functions used are _pure_ mathematical functions. 
+This implies that such functions should not alter any external state including but not limited to modifying global variables, writing data to files. (Printing might be okay, but do at discretion.)
+
+```julia
+julia> JuliaBUGS.@register_primitive function f(x)
+    return x + 1
+end
+f (generic function with 1 method)
+
+julia> JuliaBUGS.f(2)
+3
+```
+
+Users can also `introduce` a function into `JuliaBUGS`, by 
+
+```julia
+julia> f(x) = x + 1
+f (generic function with 1 method)
+
+julia> JuliaBUGS.@register_primitive(f);
+
+julia> JuliaBUGS.f(1)
+2
+```
+
+After registering the function or distributions, they can be used just like any other functions or distributions provided by BUGS.