formatting fixes

Author: alyst

src/frontend/fit/fitmeasures/chi2.jl

@@ -18,7 +18,8 @@ Return the χ² value.
     (fit.minimum - logdet(obs_cov(observed(model))) - nobserved_vars(observed(model)))
 
 # bollen, p. 115, only correct for GLS weight matrix
-χ²(lossfun::SemWLS, fit::SemFit, model::AbstractSemSingle) = (nsamples(fit) - 1) * fit.minimum
+χ²(lossfun::SemWLS, fit::SemFit, model::AbstractSemSingle) =
+    (nsamples(fit) - 1) * fit.minimum
 
 # FIML
 function χ²(lossfun::SemFIML, fit::SemFit, model::AbstractSemSingle)

src/frontend/fit/summary.jl

@@ -160,8 +160,7 @@ function sem_summary(
 
     var_array = reduce(
         hcat,
-        check_round(partable.columns[c][var_indices]; digits = digits) for
-        c in var_columns
+        check_round(partable.columns[c][var_indices]; digits = digits) for c in var_columns
     )
     var_columns[2] = Symbol("")
 

src/frontend/specification/ParameterTable.jl

@@ -235,8 +235,11 @@ sort_vars(partable::ParameterTable) = sort_vars!(deepcopy(partable))
 # add a row --------------------------------------------------------------------------------
 
 function Base.push!(partable::ParameterTable, d::Union{AbstractDict{Symbol}, NamedTuple})
-    issetequal(keys(partable.columns), keys(d)) ||
-        throw(ArgumentError("The new row needs to have the same keys as the columns of the parameter table."))
+    issetequal(keys(partable.columns), keys(d)) || throw(
+        ArgumentError(
+            "The new row needs to have the same keys as the columns of the parameter table.",
+        ),
+    )
     for (key, val) in pairs(d)
         push!(partable.columns[key], val)
     end

src/frontend/specification/RAMMatrices.jl

@@ -34,8 +34,7 @@ function observed_var_indices(ram::RAMMatrices)
     return obs_inds
 end
 
-latent_var_indices(ram::RAMMatrices) =
-    [i for i in axes(ram.F, 2) if islatent_var(ram, i)]
+latent_var_indices(ram::RAMMatrices) = [i for i in axes(ram.F, 2) if islatent_var(ram, i)]
 
 # observed variables in the order as they appear in ram.F rows
 function observed_vars(ram::RAMMatrices)
@@ -145,8 +144,10 @@ function RAMMatrices(
         @assert length(partable.sorted_vars) == nvars(partable)
         vars_sorted = copy(partable.sorted_vars)
     else
-        vars_sorted = [partable.observed_vars
-                       partable.latent_vars]
+        vars_sorted = [
+            partable.observed_vars
+            partable.latent_vars
+        ]
     end
 
     # indices of the vars (A/S/M rows or columns)
@@ -228,13 +229,20 @@ function RAMMatrices(
         sort!(M_consts, by = first)
     end
 
-    return RAMMatrices(ParamsMatrix{T}(A_inds, A_consts, (n_vars, n_vars)),
-                       ParamsMatrix{T}(S_inds, S_consts, (n_vars, n_vars)),
-                       sparse(1:n_observed,
-                              [vars_index[var] for var in partable.observed_vars],
-                              ones(T, n_observed), n_observed, n_vars),
-                       !isnothing(M_inds) ? ParamsVector{T}(M_inds, M_consts, (n_vars,)) : nothing,
-                       params, vars_sorted)
+    return RAMMatrices(
+        ParamsMatrix{T}(A_inds, A_consts, (n_vars, n_vars)),
+        ParamsMatrix{T}(S_inds, S_consts, (n_vars, n_vars)),
+        sparse(
+            1:n_observed,
+            [vars_index[var] for var in partable.observed_vars],
+            ones(T, n_observed),
+            n_observed,
+            n_vars,
+        ),
+        !isnothing(M_inds) ? ParamsVector{T}(M_inds, M_consts, (n_vars,)) : nothing,
+        params,
+        vars_sorted,
+    )
 end
 
 Base.convert(
@@ -372,10 +380,7 @@ function append_rows!(
             arr_ix = arr_ixs[arr.linear_indices[j]]
             skip_symmetric && (arr_ix ∈ visited_indices) && continue
 
-            push!(
-                partable,
-                partable_row(par, arr_ix, arr_name, varnames, free = true),
-            )
+            push!(partable, partable_row(par, arr_ix, arr_name, varnames, free = true))
             if skip_symmetric
                 # mark index and its symmetric as visited
                 push!(visited_indices, arr_ix)

src/frontend/specification/documentation.jl

@@ -1,8 +1,7 @@
 params(spec::SemSpecification) = spec.params
 
 # observed + latent
-vars(spec::SemSpecification) =
-    error("vars(spec::$(typeof(spec))) is not implemented")
+vars(spec::SemSpecification) = error("vars(spec::$(typeof(spec))) is not implemented")
 observed_vars(spec::SemSpecification) =
     error("observed_vars(spec::$(typeof(spec))) is not implemented")
 latent_vars(spec::SemSpecification) =

src/imply/RAM/generic.jl

@@ -85,8 +85,7 @@ mutable struct RAM{MS, A1, A2, A3, A4, A5, A6, V2, M1, M2, M3, M4, S1, S2, S3} <
     ∇S::S2
     ∇M::S3
 
-    RAM{MS}(args...) where {MS <: MeanStructure} =
-        new{MS, map(typeof, args)...}(args...)
+    RAM{MS}(args...) where {MS <: MeanStructure} = new{MS, map(typeof, args)...}(args...)
 end
 
 ############################################################################################
@@ -134,7 +133,11 @@ function RAM(;
     # μ
     if meanstructure
         MS = HasMeanStructure
-        !isnothing(ram_matrices.M) || throw(ArgumentError("You set `meanstructure = true`, but your model specification contains no mean parameters."))
+        !isnothing(ram_matrices.M) || throw(
+            ArgumentError(
+                "You set `meanstructure = true`, but your model specification contains no mean parameters.",
+            ),
+        )
         M_pre = materialize(ram_matrices.M, rand_params)
         ∇M = gradient_required ? sparse_gradient(ram_matrices.M) : nothing
         μ = zeros(n_obs)
@@ -167,12 +170,7 @@ end
 ### methods
 ############################################################################################
 
-function update!(
-    targets::EvaluationTargets,
-    imply::RAM,
-    model::AbstractSemSingle,
-    params,
-)
+function update!(targets::EvaluationTargets, imply::RAM, model::AbstractSemSingle, params)
     materialize!(imply.A, imply.ram_matrices.A, params)
     materialize!(imply.S, imply.ram_matrices.S, params)
     if !isnothing(imply.M)

src/imply/abstract.jl

@@ -12,7 +12,7 @@ nparams(imply::SemImply) = nparams(imply.ram_matrices)
 
 function check_acyclic(A::AbstractMatrix)
     # check if the model is acyclic
-    acyclic = isone(det(I-A))
+    acyclic = isone(det(I - A))
 
     # check if A is lower or upper triangular
     if istril(A)
@@ -23,8 +23,9 @@ function check_acyclic(A::AbstractMatrix)
         return UpperTriangular(A)
     else
         if acyclic
-            @info "Your model is acyclic, specifying the A Matrix as either Upper or Lower Triangular can have great performance benefits.\n" maxlog=1
+            @info "Your model is acyclic, specifying the A Matrix as either Upper or Lower Triangular can have great performance benefits.\n" maxlog =
+                1
         end
         return A
     end
-end
+end

src/observed/covariance.jl

@@ -33,5 +33,11 @@ function SemObservedCovariance(;
         isnothing(obs_mean) || (obs_mean = obs_mean[obs2spec_perm])
     end
 
-    return SemObservedData(nothing, Symmetric(obs_cov), obs_mean, size(obs_cov, 1), nsamples)
+    return SemObservedData(
+        nothing,
+        Symmetric(obs_cov),
+        obs_mean,
+        size(obs_cov, 1),
+        nsamples,
+    )
 end

test/examples/political_democracy/by_parts.jl

@@ -40,8 +40,12 @@ model_ridge = Sem(observed, imply_ram, SemLoss(ml, ridge), optimizer_obj)
 
 model_constant = Sem(observed, imply_ram, SemLoss(ml, constant), optimizer_obj)
 
-model_ml_weighted =
-    Sem(observed, imply_ram, SemLoss(ml; loss_weights = [nsamples(model_ml)]), optimizer_obj)
+model_ml_weighted = Sem(
+    observed,
+    imply_ram,
+    SemLoss(ml; loss_weights = [nsamples(model_ml)]),
+    optimizer_obj,
+)
 
 ############################################################################################
 ### test gradients

test/examples/political_democracy/political_democracy.jl

@@ -78,22 +78,7 @@ spec = RAMMatrices(;
     S = S,
     F = F,
     params = x,
-    vars = [
-        :x1,
-        :x2,
-        :x3,
-        :y1,
-        :y2,
-        :y3,
-        :y4,
-        :y5,
-        :y6,
-        :y7,
-        :y8,
-        :ind60,
-        :dem60,
-        :dem65,
-    ],
+    vars = [:x1, :x2, :x3, :y1, :y2, :y3, :y4, :y5, :y6, :y7, :y8, :ind60, :dem60, :dem65],
 )
 
 partable = ParameterTable(spec)
@@ -110,22 +95,7 @@ spec_mean = RAMMatrices(;
     F = F,
     M = M,
     params = [SEM.params(spec); Symbol.("x", string.(32:38))],
-    vars = [
-        :x1,
-        :x2,
-        :x3,
-        :y1,
-        :y2,
-        :y3,
-        :y4,
-        :y5,
-        :y6,
-        :y7,
-        :y8,
-        :ind60,
-        :dem60,
-        :dem65,
-    ],
+    vars = [:x1, :x2, :x3, :y1, :y2, :y3, :y4, :y5, :y6, :y7, :y8, :ind60, :dem60, :dem65],
 )
 
 partable_mean = ParameterTable(spec_mean)