formatting fixes

Author: alyst

src/additional_functions/helper.jl

@@ -41,7 +41,7 @@ function get_observed(rowind, data, semobserved; args = (), kwargs = NamedTuple(
     return observed_vec
 end
 
-skipmissing_mean(mat::AbstractMatrix) = 
+skipmissing_mean(mat::AbstractMatrix) =
     [mean(skipmissing(coldata)) for coldata in eachcol(mat)]
 
 function F_one_person(imp_mean, meandiff, inverse, data, logdet)

src/frontend/fit/summary.jl

@@ -138,8 +138,7 @@ function sem_summary(
         ),
     )
 
-    sorted_columns =
-        [:from, :parameter_type, :to, :estimate, :param, :value_fixed, :start]
+    sorted_columns = [:from, :parameter_type, :to, :estimate, :param, :value_fixed, :start]
     regression_columns = sort_partially(sorted_columns, columns)
 
     regression_array = reduce(
@@ -166,8 +165,7 @@ function sem_summary(
         (partable.columns[:to] .== partable.columns[:from]),
     )
 
-    sorted_columns =
-        [:from, :parameter_type, :to, :estimate, :param, :value_fixed, :start]
+    sorted_columns = [:from, :parameter_type, :to, :estimate, :param, :value_fixed, :start]
     variance_columns = sort_partially(sorted_columns, columns)
 
     variance_array = reduce(
@@ -194,8 +192,7 @@ function sem_summary(
         (partable.columns[:to] .!= partable.columns[:from]),
     )
 
-    sorted_columns =
-        [:from, :parameter_type, :to, :estimate, :param, :value_fixed, :start]
+    sorted_columns = [:from, :parameter_type, :to, :estimate, :param, :value_fixed, :start]
     variance_columns = sort_partially(sorted_columns, columns)
 
     variance_array = reduce(

src/frontend/specification/ParameterTable.jl

@@ -59,17 +59,8 @@ end
 ############################################################################################
 
 function Base.show(io::IO, partable::ParameterTable)
-    relevant_columns = [
-        :from,
-        :parameter_type,
-        :to,
-        :free,
-        :value_fixed,
-        :start,
-        :estimate,
-        :se,
-        :param,
-    ]
+    relevant_columns =
+        [:from, :parameter_type, :to, :free, :value_fixed, :start, :estimate, :se, :param]
     shown_columns = filter!(
         col -> haskey(partable.columns, col) && length(partable.columns[col]) > 0,
         relevant_columns,
@@ -126,6 +117,7 @@ params(partable::ParameterTable) =
 
 
 # Sorting ----------------------------------------------------------------------------------
+
 struct CyclicModelError <: Exception
     msg::AbstractString
 end
@@ -150,8 +142,8 @@ function sort_vars!(partable::ParameterTable)
     ]
 
     is_regression = [
-        (partype == :→) && (from != Symbol("1")) for
-        (partype, from) in zip(partable.columns[:parameter_type], partable.columns[:from])
+        (partype == :→) && (from != Symbol("1")) for (partype, from) in
+        zip(partable.columns[:parameter_type], partable.columns[:from])
     ]
 
     to = partable.columns[:to][is_regression]

src/frontend/specification/RAMMatrices.jl

@@ -147,8 +147,7 @@ function RAMMatrices(
     # F indices
     F_ind =
         length(partable.sorted_vars) != 0 ?
-        findall(∈(Set(partable.observed_vars)), partable.sorted_vars) :
-        1:n_observed
+        findall(∈(Set(partable.observed_vars)), partable.sorted_vars) : 1:n_observed
 
     # indices of the colnames
     colnames =

src/imply/RAM/generic.jl

@@ -65,26 +65,8 @@ Additional interfaces
 Only available in gradient! calls:
 - `I_A⁻¹(::RAM)` -> ``(I-A)^{-1}``
 """
-mutable struct RAM{
-    A1,
-    A2,
-    A3,
-    A4,
-    A5,
-    A6,
-    V2,
-    I1,
-    I2,
-    I3,
-    M1,
-    M2,
-    M3,
-    M4,
-    S1,
-    S2,
-    S3,
-    B,
-} <: SemImply
+mutable struct RAM{A1, A2, A3, A4, A5, A6, V2, I1, I2, I3, M1, M2, M3, M4, S1, S2, S3, B} <:
+               SemImply
     Σ::A1
     A::A2
     S::A3

src/loss/ML/FIML.jl

@@ -255,5 +255,4 @@ end
 
 get_n_nodes(specification::RAMMatrices) = specification.size_F[2]
 get_n_nodes(specification::ParameterTable) =
-    length(specification.observed_vars) +
-    length(specification.latent_vars)
+    length(specification.observed_vars) + length(specification.latent_vars)

src/objective_gradient_hessian.jl

@@ -38,12 +38,7 @@ function gradient_hessian!(gradient, hessian, model::AbstractSemSingle, params)
     gradient_hessian!(gradient, hessian, loss(model), params, model)
 end
 
-function objective_gradient_hessian!(
-    gradient,
-    hessian,
-    model::AbstractSemSingle,
-    params,
-)
+function objective_gradient_hessian!(gradient, hessian, model::AbstractSemSingle, params)
     fill!(gradient, zero(eltype(gradient)))
     fill!(hessian, zero(eltype(hessian)))
     objective_gradient_hessian!(imply(model), params, model)

test/examples/helper.jl

@@ -1,6 +1,5 @@
 function test_gradient(model, params; rtol = 1e-10, atol = 0)
-    true_grad =
-        FiniteDiff.finite_difference_gradient(Base.Fix1(objective!, model), params)
+    true_grad = FiniteDiff.finite_difference_gradient(Base.Fix1(objective!, model), params)
     gradient = similar(params)
 
     # F and G
@@ -250,8 +249,7 @@ function compare_estimates(
                 if type == :↔
                     type = "~~"
                 elseif type == :→
-                    if (from ∈ partable.latent_vars) &&
-                       (to ∈ partable.observed_vars)
+                    if (from ∈ partable.latent_vars) && (to ∈ partable.observed_vars)
                         type = "=~"
                     else
                         type = "~"