diff --git a/EpiAware/src/EpiLatentModels/autoregressive.jl b/EpiAware/src/EpiLatentModels/autoregressive.jl
index a6f86b996..04927cc9f 100644
--- a/EpiAware/src/EpiLatentModels/autoregressive.jl
+++ b/EpiAware/src/EpiLatentModels/autoregressive.jl
@@ -1,11 +1,6 @@
-"""
+@doc raw"
 The autoregressive (AR) model struct.
-
-# Fields
-- `damp_prior::D`: The prior distribution for the damping factor.
-- `std_prior::S`: The prior distribution for the standard deviation.
-- `init_prior::I`: The prior distribution for the initial values.
-"""
+"
 @with_kw struct AR{D <: Priors, S <: Distribution, I <: Priors} <: AbstractLatentModel
     damp_prior::D = truncated(Normal(0.0, 0.05), 0.0, 1)
     std_prior::S = truncated(Normal(0.0, 0.05), 0.0, Inf)
@@ -13,10 +8,11 @@ The autoregressive (AR) model struct.
     @assert length(damp_prior)==length(init_prior) "damp_prior and init_prior must have the same length"
 end
 
-"""
+@doc raw"
 Generate a latent AR series.
 
 # Arguments
+
 - `latent_model::AR`: The AR model.
 - `n::Int`: The length of the AR series.
 
@@ -27,7 +23,7 @@ Generate a latent AR series.
 # Notes
 - The length of `damp_prior` and `init_prior` must be the same.
 - `n` must be longer than the order of the autoregressive process.
-"""
+"
 @model function generate_latent(latent_model::AR, n)
     p = length(latent_model.damp_prior)
     ϵ_t ~ MvNormal(ones(n - p))