link function from distributions are never used (#39)

Author: guilhermebodin

Project.toml

@@ -15,10 +15,10 @@ Optim = "~0.19"
 SpecialFunctions = "~0.8"
 julia = "~1"
 
-[targets]
-test = ["Test", "Random", "HypothesisTests"]
-
 [extras]
 HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Test", "Random", "HypothesisTests"]

src/distributions/beta.jl

@@ -34,12 +34,6 @@ function log_likelihood(::Type{Beta}, y::Vector{T}, param::Vector{Vector{T}}, n:
 end
 
 # Links
-function link(::Type{Beta}, param::Vector{T}) where T 
-    return [
-        link(LogLink, param[1], zero(T));
-        link(LogLink, param[2], zero(T))
-    ]
-end
 function unlink(::Type{Beta}, param_tilde::Vector{T}) where T 
     return [
         unlink(LogLink, param_tilde[1], zero(T));

src/distributions/common_interface.jl

@@ -17,9 +17,6 @@ end
 function fisher_information(D::Type{<:Distribution}, param::Vector{T}) where T
     return error("fisher_information not implemented for $D distribution")
 end
-function link(D::Type{<:Distribution}, param::Vector{T}) where T 
-    return error("link not implemented for $D distribution")
-end
 function unlink(D::Type{<:Distribution}, param_tilde::Vector{T}) where T 
     return error("unlink not implemented for $D distribution")
 end

src/distributions/gamma.jl

@@ -31,12 +31,6 @@ function log_likelihood(::Type{Gamma}, y::Vector{T}, param::Vector{Vector{T}}, n
 end
 
 # Links
-function link(::Type{Gamma}, param::Vector{T}) where T 
-    return [
-        link(LogLink, param[1], zero(T));
-        link(LogLink, param[2], zero(T))
-    ]
-end
 function unlink(::Type{Gamma}, param_tilde::Vector{T}) where T 
     return [
         unlink(LogLink, param_tilde[1], zero(T));

src/distributions/lognormal.jl

@@ -28,12 +28,6 @@ function log_likelihood(::Type{LogNormal}, y::Vector{T}, param::Vector{Vector{T}
 end
 
 # Links
-function link(::Type{LogNormal}, param::Vector{T}) where T 
-    return [
-        link(IdentityLink, param[1]);
-        link(LogLink, param[2], zero(T))
-    ]
-end
 function unlink(::Type{LogNormal}, param_tilde::Vector{T}) where T 
     return [
         unlink(IdentityLink, param_tilde[1]);

src/distributions/normal.jl

@@ -31,12 +31,6 @@ function log_likelihood(::Type{Normal}, y::Vector{T}, param::Vector{Vector{T}},
 end
 
 # Links
-function link(::Type{Normal}, param::Vector{T}) where T 
-    return [
-        link(IdentityLink, param[1]);
-        link(LogLink, param[2], zero(T))
-    ]
-end
 function unlink(::Type{Normal}, param_tilde::Vector{T}) where T 
     return [
         unlink(IdentityLink, param_tilde[1]);

src/distributions/poisson.jl

@@ -24,7 +24,6 @@ function log_likelihood(::Type{Poisson}, y::Vector{Int}, param::Vector{Vector{T}
 end
 
 # Links
-link(::Type{Poisson}, param::Vector{T}) where T = link.(LogLink, param, zero(T))
 unlink(::Type{Poisson}, param_tilde::Vector{T}) where T = unlink.(LogLink, param_tilde, zero(T))
 jacobian_link(::Type{Poisson}, param_tilde::Vector{T}) where T = Diagonal(jacobian_link.(LogLink, param_tilde, zero(T)))
 

src/distributions/weibull.jl

@@ -28,12 +28,6 @@ function log_likelihood(::Type{Weibull}, y::Vector{T}, param::Vector{Vector{T}},
 end
 
 # Links
-function link(::Type{Weibull}, param::Vector{T}) where T 
-    return [
-        link(LogLink, param[1], zero(T));
-        link(LogLink, param[2], zero(T))
-    ]
-end
 function unlink(::Type{Weibull}, param_tilde::Vector{T}) where T 
     return [
         unlink(LogLink, param_tilde[1], zero(T));

src/gas/univariate_score_driven_recursion.jl

@@ -17,6 +17,7 @@ function score_driven_recursion(gas::GAS{D, T}, observations::Vector{T}, initial
     param_tilde = Vector{Vector{T}}(undef, n + 1)
     scores_tilde = Vector{Vector{T}}(undef, n)
 
+    # Query the biggest lag
     biggest_lag = length(initial_param_tilde)
 
     # initial_values  

src/utils.jl

@@ -43,10 +43,10 @@ function sample_observation(dist::Distribution)
     return rand(dist)
 end
 
-function find_unknowns(vec::Vector{T}) where T
-    return findall(isnan, vec)
+function find_unknowns(v::Vector{T}) where T
+    return findall(isnan, v)
 end
 
-function find_unknowns(mat::Matrix{T}) where T
-    return findall(isnan, vec(mat))
+function find_unknowns(m::Matrix{T}) where T
+    return findall(isnan, vec(m))
 end