fix formatting

Author: alyst

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/imply/RAM/generic.jl

@@ -144,7 +144,11 @@ function RAM(;
     # μ
     if meanstructure
         has_meanstructure = Val(true)
-        !isnothing(M_indices) || throw(ArgumentError("You set `meanstructure = true`, but your model specification contains no mean parameters."))
+        !isnothing(M_indices) || throw(
+            ArgumentError(
+                "You set `meanstructure = true`, but your model specification contains no mean parameters.",
+            ),
+        )
         ∇M = gradient ? matrix_gradient(M_indices, n_var) : nothing
         μ = zeros(n_obs)
     else

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/loss/ML/FIML.jl

@@ -100,7 +100,8 @@ function gradient!(semfiml::SemFIML, params, model)
 
     prepare_SemFIML!(semfiml, model)
 
-    gradient = ∇F_FIML(pattern_rows(observed(model)), semfiml, model) / nsamples(observed(model))
+    gradient =
+        ∇F_FIML(pattern_rows(observed(model)), semfiml, model) / nsamples(observed(model))
     return gradient
 end
 
@@ -112,8 +113,10 @@ function objective_gradient!(semfiml::SemFIML, params, model)
     prepare_SemFIML!(semfiml, model)
 
     objective =
-        F_FIML(pattern_rows(observed(model)), semfiml, model, params) / nsamples(observed(model))
-    gradient = ∇F_FIML(pattern_rows(observed(model)), semfiml, model) / nsamples(observed(model))
+        F_FIML(pattern_rows(observed(model)), semfiml, model, params) /
+        nsamples(observed(model))
+    gradient =
+        ∇F_FIML(pattern_rows(observed(model)), semfiml, model) / nsamples(observed(model))
 
     return objective, gradient
 end

src/observed/EM.jl

@@ -62,7 +62,7 @@ function em_mvn(
 
         if iter > max_iter_em
             done = true
-            @warn "EM Algorithm for MVN missing data did not converge. Likelihood for FIML is not interpretable. 
+            @warn "EM Algorithm for MVN missing data did not converge. Likelihood for FIML is not interpretable.
             Maybe try passing different starting values via 'start_em = ...' "
         elseif iter > 1
             # done = isapprox(ll, ll_prev; rtol = rtol)
@@ -153,7 +153,8 @@ end
 
 # use μ and Σ of full cases
 function start_em_observed(observed::SemObservedMissing; kwargs...)
-    if (length(observed.patterns[1]) == nobserved_vars(observed)) & (observed.pattern_nsamples[1] > 1)
+    if (length(observed.patterns[1]) == nobserved_vars(observed)) &
+       (observed.pattern_nsamples[1] > 1)
         μ = copy(observed.obs_mean[1])
         Σ = copy(Symmetric(observed.obs_cov[1]))
         if !isposdef(Σ)

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