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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: sunxd3

docs/make.jl

@@ -13,7 +13,9 @@ using DocumenterMermaid
 using MCMCChains
 
 # Doctest setup
-DocMeta.setdocmeta!(DynamicPPL, :DocTestSetup, :(using DynamicPPL, MCMCChains); recursive=true)
+DocMeta.setdocmeta!(
+    DynamicPPL, :DocTestSetup, :(using DynamicPPL, MCMCChains); recursive=true
+)
 
 makedocs(;
     sitename="DynamicPPL",

docs/src/api.md

@@ -114,21 +114,21 @@ predict
 
 The `predict` function has two main methods:
 
-1. For `AbstractVector{<:AbstractVarInfo}` - useful when you have a collection of `VarInfo` objects representing posterior samples.
-2. For `MCMCChains.Chains` - useful when you have posterior samples in the form of a `Chains` object from MCMCChains.jl.
+ 1. For `AbstractVector{<:AbstractVarInfo}` - useful when you have a collection of `VarInfo` objects representing posterior samples.
+ 2. For `MCMCChains.Chains` - useful when you have posterior samples in the form of a `Chains` object from MCMCChains.jl.
 
 ### Basic Usage
 
 The typical workflow for posterior prediction involves:
 
-1. Fitting a model to observed data to obtain posterior samples
-2. Creating a new model instance with some variables marked as missing (unobserved)
-3. Using `predict` to generate samples for these missing variables based on the posterior parameter samples
+ 1. Fitting a model to observed data to obtain posterior samples
+ 2. Creating a new model instance with some variables marked as missing (unobserved)
+ 3. Using `predict` to generate samples for these missing variables based on the posterior parameter samples
 
 When using `predict` with `MCMCChains.Chains`, you can control which variables are included in the output with the `include_all` parameter:
 
-- `include_all=false` (default): Include only newly predicted variables
-- `include_all=true`: Include both parameters from the original chain and predicted variables
+  - `include_all=false` (default): Include only newly predicted variables
+  - `include_all=true`: Include both parameters from the original chain and predicted variables
 
 ## Models within models