diff --git a/docs/src/methods/exported/assessment.md b/docs/src/methods/exported/assessment.md
index 4f8b0d65..295e9861 100644
--- a/docs/src/methods/exported/assessment.md
+++ b/docs/src/methods/exported/assessment.md
@@ -2,6 +2,5 @@
 
 ```@autodocs
 Modules = [Psychometrics]
-Pages   = ["examinee/assessment/assessment.jl",
-    ]
+Pages   = ["examinee/assessment/assessment.jl"]
 ```
\ No newline at end of file
diff --git a/docs/src/methods/exported/likelihood.md b/docs/src/methods/exported/likelihood.md
index 939e5cfd..55a260aa 100644
--- a/docs/src/methods/exported/likelihood.md
+++ b/docs/src/methods/exported/likelihood.md
@@ -5,11 +5,6 @@
 ```
 
 ```@docs
-log_likelihood(
-    response_val::Float64,
-    latent_val::Float64,
-    parameters::AbstractParametersBinary,
-)
 log_likelihood(
     response::AbstractResponse,
     examinee::AbstractExaminee,
diff --git a/docs/src/methods/exported/online.md b/docs/src/methods/exported/online.md
index 65f1b4b5..039db31d 100644
--- a/docs/src/methods/exported/online.md
+++ b/docs/src/methods/exported/online.md
@@ -2,6 +2,6 @@
 
 ```@autodocs
 Modules = [Psychometrics]
-Pages   = ["online/online.jl"]
+Pages   = ["online/routing.jl", "online/design/A_gain.jl", "online/design/A_inv_gain.jl", "online/design/A.jl", "online/design/D_gain.jl", "online/design/D_inv_gain.jl", "online/design/D.jl"]
 ```
 
diff --git a/docs/src/methods/internals/likelihood.md b/docs/src/methods/internals/likelihood.md
index 25bdb81b..0c3bebbc 100644
--- a/docs/src/methods/internals/likelihood.md
+++ b/docs/src/methods/internals/likelihood.md
@@ -4,14 +4,30 @@
     CurrentModule = Psychometrics
 ```
 
-## Using Structs
-
 ```@docs
-_likelihood(
+__likelihood(
+    response_val::Float64,
+    latent_val::Float64,
+    parameters::AbstractParametersBinary;
+    weight::Float64 = 1.0
+)
+__log_likelihood(
     response_val::Float64,
     latent_val::Float64,
     parameters::AbstractParametersBinary,
 )
+_likelihood(
+    response_val::Float64,
+    latent_val::Float64,
+    item::AbstractItem;
+    weight::Float64 = 1.0
+)
+_likelihood(
+    response::AbstractResponse,
+    latent_val::Float64,
+    item::AbstractItem;
+    weight::Float64 = 1.0
+)
 _log_likelihood(
     response_val::Float64,
     latent::Latent1D,
diff --git a/src/examinee/examinees.jl b/src/examinee/examinees.jl
index 8c19828a..7bba3b14 100644
--- a/src/examinee/examinees.jl
+++ b/src/examinee/examinees.jl
@@ -1,7 +1,7 @@
 """
     get_latents(examinees::Vector{<:AbstractExaminee})
 
-    #Description 
+#Description 
 
 Returns the latent objects of a vector of examinees as a vector of subtypes of `AbstractLatent`.
 """
@@ -13,7 +13,7 @@ end
 """
     get_latents_vals(examinees::Vector{<:AbstractExaminee})
 
-    #Description 
+#Description 
 
 Returns a matrix with latent values displayed by row.
 """
diff --git a/src/information/information.jl b/src/information/information.jl
index cd0c65a6..e170d946 100644
--- a/src/information/information.jl
+++ b/src/information/information.jl
@@ -49,7 +49,7 @@ A `Float64` vector.
 
 Compute the Fisher information for the latent/s of the examinees (second derivatives of the likelihood with respect to ``\\theta``) and each item.
 
-``E_\\theta_n \\[ I(\\theta_n | b_i) \\] ``
+\$\$E_\\theta_n \\[ I(\\theta_n | b_i) \\] \$\$
 
 ```julia
 examinee = Examinee(); #default examinee factory
@@ -99,12 +99,10 @@ end
 #   Item expected information            #
 ##########################################
 """
-```julia
-item_expected_information(
-    item::AbstractItem,
-    examinee::AbstractExaminee,
-)
-```
+    item_expected_information(
+        item::AbstractItem,
+        examinee::AbstractExaminee,
+    )
 
 # Description
 
@@ -271,12 +269,12 @@ See the docs of [`latent_information(examinee::AbstractExaminee, item::AbstractI
 
 # Arguments
 
-- **`latents_matrix::Matrix{Float64}`** : Required. A ``n_latents \\times N`` matrix with latents values. 
-- **`parameters_matrix::Matrix{Float64}`** : Required. A ``(n_latents + 1) \\times I`` matrix with item parameters. intercept (b) must be in first row, latents coefficients ``(a_j)`` in next rows ``(2, \\ldots, n_latents + 1)``. 
+- **`latents_matrix::Matrix{Float64}`** : Required. A \$n\_latents \\times N\$ matrix with latents values. 
+- **`parameters_matrix::Matrix{Float64}`** : Required. A \$(n\_latents + 1) \\times I\$ matrix with item parameters. intercept (b) must be in first row, latents coefficients ``(a_j)`` in next rows ``(2, \\ldots, n_latents + 1)``. 
     
 # Output
 
-A ``I \\time N`` `Float64` matrix. 
+A ``I \\times N`` `Float64` matrix. 
 """
 function latent_information(
     latents_matrix::Matrix{Float64},
@@ -306,12 +304,12 @@ It follows the parametrization ``a \\theta - b ``.
 
 # Arguments
 
-- **`latents_matrix::Matrix{Float64}`** : Required. A `n_latents x N` matrix with latents values. 
-- **`parameters_matrix::Matrix{Float64}`** : Required. A `(n_latents + 1) x I` matrix with item parameters. intercept (b) must be in first row, latents coefficients (a_j) in next rows (2, ..., n_latents + 1). 
+- **`latents_matrix::Matrix{Float64}`** : Required. A \$n\_latents \\times N\$ matrix with latents values. 
+- **`parameters_matrix::Matrix{Float64}`** : Required. A \$(n\_latents + 1) \\times I\$ matrix with item parameters. intercept (b) must be in first row, latents coefficients (a_j) in next rows (2, ..., n_latents + 1). 
 
 # Output
 
-A `I x N` `Float64` matrix. 
+A ``I \\times N`` `Float64` matrix. 
 """
 function latent_information_3PL(
     latents_matrix::Matrix{Float64},
diff --git a/src/online/design/D_gain.jl b/src/online/design/D_gain.jl
index 38a39d0d..a295feea 100644
--- a/src/online/design/D_gain.jl
+++ b/src/online/design/D_gain.jl
@@ -17,18 +17,18 @@ end
 """
     D_gain_method(item::AbstractItem, examinee::AbstractExaminee)
 
-    # Description
-    Computes the gain in the determinant of the expected information matrix for an item.
-    
-    # Arguments
-    - **`item::AbstractItem`**: The item.
-    - **`examinee::AbstractExaminee`**: The examinee at which computing the information.
-    
-    # Output
-    It returns a `Float64` scalar.
-    
-    # References
-    __Ren H, van der Linden WJ, Diao Q. Continuous online item calibration: Parameter recovery and item calibration. Psychometrika. 2017;82:498–522. doi: 10.1007/s11336-017-9553-1.__
+# Description
+Computes the gain in the determinant of the expected information matrix for an item.
+   
+# Arguments
+- **`item::AbstractItem`**: The item.
+- **`examinee::AbstractExaminee`**: The examinee at which computing the information.
+  
+# Output
+It returns a `Float64` scalar.
+   
+# References
+__Ren H, van der Linden WJ, Diao Q. Continuous online item calibration: Parameter recovery and item calibration. Psychometrika. 2017;82:498–522. doi: 10.1007/s11336-017-9553-1.__
 """
 function D_gain_method(item::AbstractItem, examinee::AbstractExaminee)
     return _D_gain_method(item.parameters, examinee.latent)
diff --git a/src/probability.jl b/src/probability.jl
index a991e311..70ee098d 100644
--- a/src/probability.jl
+++ b/src/probability.jl
@@ -427,7 +427,7 @@ It follows the parametrization ``a \\theta - b ``.
 # Arguments
 
 - **`parameters_matrix::Matrix{Float64}`** : Required. A `I x (n_latents + 1)` matrix with item parameters. intercept (b) must be in first column, latents coefficients (a_j) in next columns (2, ..., n_latents + 1). 
-- **`latent_matrix::Matrix{Float64}`** : Required. A `n_latents x N` or `(n_latents + 1) x N` matrix with latents values. 
+- **`latent_matrix::Matrix{Float64}`** : Required. A \$n\_latents \\times N\$ or `(n_latents + 1) x N` matrix with latents values. 
 
 # Output
 
@@ -455,7 +455,7 @@ It follows the parametrization ``a \\theta - b ``.
 # Arguments
 
 - **`parameters_matrix::Matrix{Float64}`** : Required. A `I x (n_latents + 1)` matrix with item parameters. intercept (b) must be in first column, latents coefficients (a_j) in next columns (2, ..., n_latents + 1). 
-- **`latent_matrix::Matrix{Float64}`** : Required. A `n_latents x N` matrix with latents values. 
+- **`latent_matrix::Matrix{Float64}`** : Required. A \$n\_latents \\times N\$ matrix with latents values. 
 
 # Output