From e04ad3f19e5bbe4d0a86ffb236c0c7b1203c9b2e Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 10 Jun 2022 20:53:47 +0200
Subject: [PATCH 01/15] WIP

---
 src/regressionmodel.jl  |  2 +-
 src/statisticalmodel.jl | 11 +++++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/src/regressionmodel.jl b/src/regressionmodel.jl
index d7a2afc..419263f 100644
--- a/src/regressionmodel.jl
+++ b/src/regressionmodel.jl
@@ -46,7 +46,7 @@ function modelmatrix end
 Return `X'X` where `X` is the model matrix of `model`.
 This function will return a pre-computed matrix stored in `model` if possible.
 """
-crossmodelmatrix(model::RegressionModel) = (x = modelmatrix(model); Symmetric(x' * x))
+crossmodelmatrix(model::RegressionModel; kwarg...) = (x = modelmatrix(model; kwarg...); Symmetric(x' * x))
 
 """
     leverage(model::RegressionModel)
diff --git a/src/statisticalmodel.jl b/src/statisticalmodel.jl
index e8f4f68..8e3d654 100644
--- a/src/statisticalmodel.jl
+++ b/src/statisticalmodel.jl
@@ -313,4 +313,15 @@ function adjr2(model::StatisticalModel, variant::Symbol)
     end
 end
 
+
+"""
+    momentmatrix(model::StatisticalModel)
+    
+Return the moment matrix. 
+
+For linear regression models `lm`, the moment matrix is residual(lm)*momentmatrix(lm). 
+
+"""
+function momentmatrix end
+
 const adjr² = adjr2

From 6f6a160a70ffd442a407ab5c77b473dc456af519 Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Wed, 15 Jun 2022 19:10:01 +0200
Subject: [PATCH 02/15] Fix doc

---
 src/statisticalmodel.jl | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/statisticalmodel.jl b/src/statisticalmodel.jl
index 8e3d654..533a6ed 100644
--- a/src/statisticalmodel.jl
+++ b/src/statisticalmodel.jl
@@ -313,15 +313,14 @@ function adjr2(model::StatisticalModel, variant::Symbol)
     end
 end
 
+const adjr² = adjr2
 
 """
     momentmatrix(model::StatisticalModel)
     
-Return the moment matrix. 
+Return the matrix containing the estimating equation.
 
-For linear regression models `lm`, the moment matrix is residual(lm)*momentmatrix(lm). 
+For linear regression models, the moment matrix is given by u*X, where u is the vector of residuals and X is the model matrix.
 
 """
 function momentmatrix end
-
-const adjr² = adjr2

From 0625c3c12adfca65ef12db5f260d9dbf67d6ef81 Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Thu, 16 Jun 2022 22:23:14 +0200
Subject: [PATCH 03/15] WIP

---
 src/regressionmodel.jl   | 19 +++++++++++++------
 src/statisticalmodel.jl  |  2 +-
 test/regressionmodel.jl  | 19 +++++++++++++++++++
 test/statisticalmodel.jl |  1 +
 4 files changed, 34 insertions(+), 7 deletions(-)

diff --git a/src/regressionmodel.jl b/src/regressionmodel.jl
index 419263f..c4aef4e 100644
--- a/src/regressionmodel.jl
+++ b/src/regressionmodel.jl
@@ -34,19 +34,26 @@ Return the mean of the response.
 function meanresponse end
 
 """
-    modelmatrix(model::RegressionModel)
+    modelmatrix(model::RegressionModel; weighted::Bool=false)
 
-Return the model matrix (a.k.a. the design matrix).
+Return the model matrix (a.k.a. the design matrix) or, if `weighted=true` the weighted 
+model matrix, i.e, X'sqrt.(W), where `W` is the diagonal matrix whose elements are 
+the model weights. 
 """
-function modelmatrix end
+function modelmatrix(model::RegressionModel; weighted::Bool=false) 
+end
 
 """
-    crossmodelmatrix(model::RegressionModel)
+    crossmodelmatrix(model::RegressionModel; weighted::Bool=false)
 
-Return `X'X` where `X` is the model matrix of `model`.
+Return `X'X` where `X` is the model matrix of `model` or, if `weighted=true`, `X'WX`, 
+where `W` is the diagonal matrix whose elements are  the model weights. 
 This function will return a pre-computed matrix stored in `model` if possible.
 """
-crossmodelmatrix(model::RegressionModel; kwarg...) = (x = modelmatrix(model; kwarg...); Symmetric(x' * x))
+function crossmodelmatrix(model::RegressionModel; weighted::Bool=false) 
+    x = weighted ? modelmatrix(model; weighted=weighted) : modelmatrix(model)
+    return Symmetric(x' * x)
+end
 
 """
     leverage(model::RegressionModel)
diff --git a/src/statisticalmodel.jl b/src/statisticalmodel.jl
index 533a6ed..7b2e201 100644
--- a/src/statisticalmodel.jl
+++ b/src/statisticalmodel.jl
@@ -320,7 +320,7 @@ const adjr² = adjr2
     
 Return the matrix containing the estimating equation.
 
-For linear regression models, the moment matrix is given by u*X, where u is the vector of residuals and X is the model matrix.
+For linear regression models, the moment matrix is given by `u*X`, where `u` is the vector of residuals and `X` is the model matrix.
 
 """
 function momentmatrix end
diff --git a/test/regressionmodel.jl b/test/regressionmodel.jl
index a8892fa..4b48a27 100644
--- a/test/regressionmodel.jl
+++ b/test/regressionmodel.jl
@@ -6,13 +6,32 @@ using StatsAPI: RegressionModel, crossmodelmatrix
 struct MyRegressionModel <: RegressionModel
 end
 
+struct ItsRegressionModel <: RegressionModel
+    wts
+end
+
 StatsAPI.modelmatrix(::MyRegressionModel) = [1 2; 3 4]
 
+function StatsAPI.modelmatrix(r::ItsRegressionModel; weighted::Bool=false)  
+    X = [1 2; 3 4]
+    weighted ? sqrt.(r.wts).*X : X 
+end
+
+w = [0.3, 0.2]
+
 @testset "TestRegressionModel" begin
     m = MyRegressionModel()
+    r = ItsRegressionModel(w)
 
     @test crossmodelmatrix(m) == [10 14; 14 20]
+    @test crossmodelmatrix(m; weighted=false) == [10 14; 14 20]
     @test crossmodelmatrix(m) isa Symmetric
+
+    @test crossmodelmatrix(r) == [10 14; 14 20]
+    @test crossmodelmatrix(r; weighted=false) == [10 14; 14 20]
+    @test crossmodelmatrix(r; weighted=true) ≈ [2.1 3.0; 3.0 4.4]
+    @test crossmodelmatrix(r; weighted=true) isa Symmetric
+
 end
 
 end # module TestRegressionModel
\ No newline at end of file
diff --git a/test/statisticalmodel.jl b/test/statisticalmodel.jl
index 114a68e..666f61a 100644
--- a/test/statisticalmodel.jl
+++ b/test/statisticalmodel.jl
@@ -13,6 +13,7 @@ StatsAPI.deviance(::MyStatisticalModel) = 25
 StatsAPI.nulldeviance(::MyStatisticalModel) = 40
 StatsAPI.dof(::MyStatisticalModel) = 5
 StatsAPI.nobs(::MyStatisticalModel) = 100
+StatsAPI.momentmatrix(::MyStatisticalModel) = [1 2; 3 4; 5 6; 7 8]
 
 @testset "StatisticalModel" begin
     m = MyStatisticalModel()

From e514db0186ede4a334483a41992ae86e71a84e3b Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 17 Jun 2022 00:34:09 +0200
Subject: [PATCH 04/15] Update src/regressionmodel.jl

Co-authored-by: Milan Bouchet-Valat <nalimilan@club.fr>
---
 src/regressionmodel.jl | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/regressionmodel.jl b/src/regressionmodel.jl
index c4aef4e..e5b6278 100644
--- a/src/regressionmodel.jl
+++ b/src/regressionmodel.jl
@@ -40,8 +40,7 @@ Return the model matrix (a.k.a. the design matrix) or, if `weighted=true` the we
 model matrix, i.e, X'sqrt.(W), where `W` is the diagonal matrix whose elements are 
 the model weights. 
 """
-function modelmatrix(model::RegressionModel; weighted::Bool=false) 
-end
+function modelmatrix(model::RegressionModel; weighted::Bool=false) end
 
 """
     crossmodelmatrix(model::RegressionModel; weighted::Bool=false)

From bfe8ac67381ec62c35c9ba0a808e295425e84986 Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 17 Jun 2022 00:34:19 +0200
Subject: [PATCH 05/15] Update src/regressionmodel.jl

Co-authored-by: Milan Bouchet-Valat <nalimilan@club.fr>
---
 src/regressionmodel.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/regressionmodel.jl b/src/regressionmodel.jl
index e5b6278..3eaa497 100644
--- a/src/regressionmodel.jl
+++ b/src/regressionmodel.jl
@@ -46,7 +46,7 @@ function modelmatrix(model::RegressionModel; weighted::Bool=false) end
     crossmodelmatrix(model::RegressionModel; weighted::Bool=false)
 
 Return `X'X` where `X` is the model matrix of `model` or, if `weighted=true`, `X'WX`, 
-where `W` is the diagonal matrix whose elements are  the model weights. 
+where `W` is the diagonal matrix whose elements are the model weights. 
 This function will return a pre-computed matrix stored in `model` if possible.
 """
 function crossmodelmatrix(model::RegressionModel; weighted::Bool=false) 

From af658881a3b4ccbb809e392a66532b1716125cf2 Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 17 Jun 2022 00:34:47 +0200
Subject: [PATCH 06/15] Update src/statisticalmodel.jl

Co-authored-by: Milan Bouchet-Valat <nalimilan@club.fr>
---
 src/statisticalmodel.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/statisticalmodel.jl b/src/statisticalmodel.jl
index 7b2e201..1babd65 100644
--- a/src/statisticalmodel.jl
+++ b/src/statisticalmodel.jl
@@ -320,7 +320,7 @@ const adjr² = adjr2
     
 Return the matrix containing the estimating equation.
 
-For linear regression models, the moment matrix is given by `u*X`, where `u` is the vector of residuals and `X` is the model matrix.
-
+For linear regression models, the moment matrix is given by `u*X`,
+where `u` is the vector of residuals and `X` is the model matrix.
 """
 function momentmatrix end

From 11505ff462311b123b7266d6dcdf598b7978cb5a Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 17 Jun 2022 00:38:46 +0200
Subject: [PATCH 07/15] Change type name for clarity

---
 test/regressionmodel.jl | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/test/regressionmodel.jl b/test/regressionmodel.jl
index 4b48a27..9268bfa 100644
--- a/test/regressionmodel.jl
+++ b/test/regressionmodel.jl
@@ -6,22 +6,22 @@ using StatsAPI: RegressionModel, crossmodelmatrix
 struct MyRegressionModel <: RegressionModel
 end
 
-struct ItsRegressionModel <: RegressionModel
-    wts
+struct MyWeightedRegressionModel <: RegressionModel
+    wts::AbstractVector
 end
 
 StatsAPI.modelmatrix(::MyRegressionModel) = [1 2; 3 4]
 
-function StatsAPI.modelmatrix(r::ItsRegressionModel; weighted::Bool=false)  
+function StatsAPI.modelmatrix(r::MyWeightedRegressionModel; weighted::Bool=false)
     X = [1 2; 3 4]
-    weighted ? sqrt.(r.wts).*X : X 
+    weighted ? sqrt.(r.wts).*X : X
 end
 
 w = [0.3, 0.2]
 
 @testset "TestRegressionModel" begin
     m = MyRegressionModel()
-    r = ItsRegressionModel(w)
+    r = MyWeightedRegressionModel(w)
 
     @test crossmodelmatrix(m) == [10 14; 14 20]
     @test crossmodelmatrix(m; weighted=false) == [10 14; 14 20]
@@ -31,7 +31,6 @@ w = [0.3, 0.2]
     @test crossmodelmatrix(r; weighted=false) == [10 14; 14 20]
     @test crossmodelmatrix(r; weighted=true) ≈ [2.1 3.0; 3.0 4.4]
     @test crossmodelmatrix(r; weighted=true) isa Symmetric
-
 end
 
 end # module TestRegressionModel
\ No newline at end of file

From de0c6aee338d24d4a6cfefeb94fb367d62e6ca3e Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 17 Jun 2022 00:41:07 +0200
Subject: [PATCH 08/15] Update src/regressionmodel.jl

Co-authored-by: Milan Bouchet-Valat <nalimilan@club.fr>
---
 src/regressionmodel.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/regressionmodel.jl b/src/regressionmodel.jl
index 3eaa497..f8f12aa 100644
--- a/src/regressionmodel.jl
+++ b/src/regressionmodel.jl
@@ -37,8 +37,8 @@ function meanresponse end
     modelmatrix(model::RegressionModel; weighted::Bool=false)
 
 Return the model matrix (a.k.a. the design matrix) or, if `weighted=true` the weighted 
-model matrix, i.e, X'sqrt.(W), where `W` is the diagonal matrix whose elements are 
-the model weights. 
+model matrix, i.e. `X' * sqrt.(W)`, where `X` is the model matrix and
+`W` is the diagonal matrix whose elements are the model weights. 
 """
 function modelmatrix(model::RegressionModel; weighted::Bool=false) end
 

From c36352bd65a630a333c724be8aafb1bf6d54e730 Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 17 Jun 2022 00:41:43 +0200
Subject: [PATCH 09/15] Don't test momentmatrix

---
 test/statisticalmodel.jl | 1 -
 1 file changed, 1 deletion(-)

diff --git a/test/statisticalmodel.jl b/test/statisticalmodel.jl
index 666f61a..114a68e 100644
--- a/test/statisticalmodel.jl
+++ b/test/statisticalmodel.jl
@@ -13,7 +13,6 @@ StatsAPI.deviance(::MyStatisticalModel) = 25
 StatsAPI.nulldeviance(::MyStatisticalModel) = 40
 StatsAPI.dof(::MyStatisticalModel) = 5
 StatsAPI.nobs(::MyStatisticalModel) = 100
-StatsAPI.momentmatrix(::MyStatisticalModel) = [1 2; 3 4; 5 6; 7 8]
 
 @testset "StatisticalModel" begin
     m = MyStatisticalModel()

From 9a7b2ab2d44eca9d3efc8aa3c9d5f70f617b183d Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 17 Jun 2022 00:44:09 +0200
Subject: [PATCH 10/15] Don't test momentmatrix

---
 test/statisticalmodel.jl | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/test/statisticalmodel.jl b/test/statisticalmodel.jl
index 666f61a..4b35998 100644
--- a/test/statisticalmodel.jl
+++ b/test/statisticalmodel.jl
@@ -13,7 +13,6 @@ StatsAPI.deviance(::MyStatisticalModel) = 25
 StatsAPI.nulldeviance(::MyStatisticalModel) = 40
 StatsAPI.dof(::MyStatisticalModel) = 5
 StatsAPI.nobs(::MyStatisticalModel) = 100
-StatsAPI.momentmatrix(::MyStatisticalModel) = [1 2; 3 4; 5 6; 7 8]
 
 @testset "StatisticalModel" begin
     m = MyStatisticalModel()
@@ -37,4 +36,4 @@ StatsAPI.momentmatrix(::MyStatisticalModel) = [1 2; 3 4; 5 6; 7 8]
     @test adjr2 === adjr²
 end
 
-end # module TestStatisticalModel
\ No newline at end of file
+end # module TestStatisticalModel

From 48333e50977fdd7f22db75ac50c6196aae11eacf Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Fri, 17 Jun 2022 14:47:15 +0200
Subject: [PATCH 11/15] Update src/regressionmodel.jl

Co-authored-by: Moritz Schauer <moritzschauer@web.de>
---
 src/regressionmodel.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/regressionmodel.jl b/src/regressionmodel.jl
index f8f12aa..ff8c6dd 100644
--- a/src/regressionmodel.jl
+++ b/src/regressionmodel.jl
@@ -45,7 +45,7 @@ function modelmatrix(model::RegressionModel; weighted::Bool=false) end
 """
     crossmodelmatrix(model::RegressionModel; weighted::Bool=false)
 
-Return `X'X` where `X` is the model matrix of `model` or, if `weighted=true`, `X'WX`, 
+Return `X'X` where `X` is the model/design matrix of `model` or, if `weighted=true`, `X'WX`, 
 where `W` is the diagonal matrix whose elements are the model weights. 
 This function will return a pre-computed matrix stored in `model` if possible.
 """

From e927f72e89d43d4fe8fecf9bdd10d6b6b66cfdfd Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Sat, 18 Jun 2022 13:07:35 +0200
Subject: [PATCH 12/15] Docs fixes

---
 src/regressionmodel.jl  | 16 +++++++++-------
 src/statisticalmodel.jl | 19 +++++++++++++++----
 2 files changed, 24 insertions(+), 11 deletions(-)

diff --git a/src/regressionmodel.jl b/src/regressionmodel.jl
index ff8c6dd..1e7fa45 100644
--- a/src/regressionmodel.jl
+++ b/src/regressionmodel.jl
@@ -36,9 +36,9 @@ function meanresponse end
 """
     modelmatrix(model::RegressionModel; weighted::Bool=false)
 
-Return the model matrix (a.k.a. the design matrix) or, if `weighted=true` the weighted 
-model matrix, i.e. `X' * sqrt.(W)`, where `X` is the model matrix and
-`W` is the diagonal matrix whose elements are the model weights. 
+Return the model matrix (design matrix) or, if `weighted=true` the weighted 
+model matrix, i.e. `X * sqrt.(W)`, where `X` is the model matrix and
+`W` is the diagonal matrix whose elements are the [model weights](@ref weights(::StatisticalModel)). 
 """
 function modelmatrix(model::RegressionModel; weighted::Bool=false) end
 
@@ -46,7 +46,7 @@ function modelmatrix(model::RegressionModel; weighted::Bool=false) end
     crossmodelmatrix(model::RegressionModel; weighted::Bool=false)
 
 Return `X'X` where `X` is the model/design matrix of `model` or, if `weighted=true`, `X'WX`, 
-where `W` is the diagonal matrix whose elements are the model weights. 
+where `W` is the diagonal matrix whose elements are the [model weights](@ref weights(::StatisticalModel)). 
 This function will return a pre-computed matrix stored in `model` if possible.
 """
 function crossmodelmatrix(model::RegressionModel; weighted::Bool=false) 
@@ -71,11 +71,13 @@ of each data point.
 function cooksdistance end
 
 """
-    residuals(model::RegressionModel)
+    residuals(model::RegressionModel; weighted::Bool=false)
+
+Return the residuals of the model or, if `weighted=true`, the residuals multiplied by
+the square root of the [model weights](@ref weights(::StatisticalModel)). 
 
-Return the residuals of the model.
 """
-function residuals end
+function residuals(model::RegressionModel; weighted::Bool=false) end
 
 """
     predict(model::RegressionModel, [newX])
diff --git a/src/statisticalmodel.jl b/src/statisticalmodel.jl
index 1babd65..fafb8fa 100644
--- a/src/statisticalmodel.jl
+++ b/src/statisticalmodel.jl
@@ -317,10 +317,21 @@ const adjr² = adjr2
 
 """
     momentmatrix(model::StatisticalModel)
-    
-Return the matrix containing the estimating equation.
 
-For linear regression models, the moment matrix is given by `u*X`,
-where `u` is the vector of residuals and `X` is the model matrix.
+Return the matrix containing the empirical moments defining the estimated parameters.
+
+For likelihood-based models, `momentmatrix` returns the log-score, i.e. the gradient
+of the log-likelihood evaluated at each observation. For semiparametric models, each row
+of the `(n×k)` matrix returned by `momentmatrix` is `m(Zᵢ, β̂)`, where `m(⋅, ⋅)`
+is a vector-valued function evaluated at the estimated parameter `β̂` and `Zᵢ` is the 
+vector of data for entity `i`. The vector-valued function satisfies `∑ᵢm(Zᵢ, β̂) = 0`.
+
+For linear and generalized linear models, the parameters of interest are the coefficients
+of the linear predictor. The moment matrix of a linear model is given by `u.*X`,
+where `u` is the vector of residuals and `X` is the model matrix. The moment matrix of
+a generalized linear model with link function `g`, the moment matrix is `X'e`, where `e`
+is given by `Y-g⁻¹(X'β̂)` where `X` is the model matrix, `Y` is the model response, and
+`β̂` is the vector of estimated coefficients. 
 """
 function momentmatrix end
+

From ab928fa206e0956dce2135e72b12c25221edac6c Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Tue, 21 Jun 2022 16:51:13 +0200
Subject: [PATCH 13/15] Improve docs

---
 src/statisticalmodel.jl | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/statisticalmodel.jl b/src/statisticalmodel.jl
index fafb8fa..2539420 100644
--- a/src/statisticalmodel.jl
+++ b/src/statisticalmodel.jl
@@ -322,16 +322,16 @@ Return the matrix containing the empirical moments defining the estimated parame
 
 For likelihood-based models, `momentmatrix` returns the log-score, i.e. the gradient
 of the log-likelihood evaluated at each observation. For semiparametric models, each row
-of the `(n×k)` matrix returned by `momentmatrix` is `m(Zᵢ, β̂)`, where `m(⋅, ⋅)`
-is a vector-valued function evaluated at the estimated parameter `β̂` and `Zᵢ` is the 
-vector of data for entity `i`. The vector-valued function satisfies `∑ᵢm(Zᵢ, β̂) = 0`.
+of the ``(n \\times k)`` matrix returned by `momentmatrix` is ``m(Z_i, b)``, where `m`
+is a vector-valued function evaluated at the estimated parameter `b` and ``Z_i`` is the 
+vector of data for entity `i`. The vector-valued function satisfies ``\\sum_{i=1}^n m(Z_i, b) = 0``.
 
 For linear and generalized linear models, the parameters of interest are the coefficients
 of the linear predictor. The moment matrix of a linear model is given by `u.*X`,
 where `u` is the vector of residuals and `X` is the model matrix. The moment matrix of
 a generalized linear model with link function `g`, the moment matrix is `X'e`, where `e`
-is given by `Y-g⁻¹(X'β̂)` where `X` is the model matrix, `Y` is the model response, and
-`β̂` is the vector of estimated coefficients. 
+is given by ``Y-g^{-1}(X'b)`` where `X` is the model matrix, `Y` is the model response, and
+`b` is the vector of estimated coefficients. 
 """
 function momentmatrix end
 

From 2858ba0736a9eba01382531e970f20c3da7feba2 Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Wed, 19 Oct 2022 11:16:54 +0200
Subject: [PATCH 14/15] Update src/regressionmodel.jl to reflect reviewer
 suggestions

Co-authored-by: Milan Bouchet-Valat <nalimilan@club.fr>
---
 src/regressionmodel.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/regressionmodel.jl b/src/regressionmodel.jl
index 1e7fa45..25e9a66 100644
--- a/src/regressionmodel.jl
+++ b/src/regressionmodel.jl
@@ -40,7 +40,7 @@ Return the model matrix (design matrix) or, if `weighted=true` the weighted
 model matrix, i.e. `X * sqrt.(W)`, where `X` is the model matrix and
 `W` is the diagonal matrix whose elements are the [model weights](@ref weights(::StatisticalModel)). 
 """
-function modelmatrix(model::RegressionModel; weighted::Bool=false) end
+function modelmatrix end
 
 """
     crossmodelmatrix(model::RegressionModel; weighted::Bool=false)

From 06206add77e2d6fce3e07e738cbb61d174814c50 Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Wed, 19 Oct 2022 11:28:56 +0200
Subject: [PATCH 15/15] Update src/statisticalmodel.jl

Co-authored-by: Milan Bouchet-Valat <nalimilan@club.fr>
---
 src/statisticalmodel.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/statisticalmodel.jl b/src/statisticalmodel.jl
index 2539420..9680ecc 100644
--- a/src/statisticalmodel.jl
+++ b/src/statisticalmodel.jl
@@ -329,7 +329,7 @@ vector of data for entity `i`. The vector-valued function satisfies ``\\sum_{i=1
 For linear and generalized linear models, the parameters of interest are the coefficients
 of the linear predictor. The moment matrix of a linear model is given by `u.*X`,
 where `u` is the vector of residuals and `X` is the model matrix. The moment matrix of
-a generalized linear model with link function `g`, the moment matrix is `X'e`, where `e`
+a a generalized linear model with link function `g` is `X'e`, where `e`
 is given by ``Y-g^{-1}(X'b)`` where `X` is the model matrix, `Y` is the model response, and
 `b` is the vector of estimated coefficients. 
 """