fix dangling whitespace

Author: alyst

src/additional_functions/helper.jl

@@ -10,7 +10,7 @@ function neumann_series(mat::SparseMatrixCSC)
     return inverse
 end
 
-#= 
+#=
 function make_onelement_array(A)
     isa(A, Array) ? nothing : (A = [A])
     return A

src/additional_functions/start_val/start_fabin3.jl

@@ -1,6 +1,6 @@
 """
     start_fabin3(model)
-    
+
 Return a vector of FABIN 3 starting values (see Hägglund 1982).
 Not available for ensemble models.
 """
@@ -58,8 +58,8 @@ function start_fabin3(ram_matrices::RAMMatrices, Σ, μ)
         if !isnothing(M)
             in_M = length.(M_ind) .!= 0
             in_any = in_A .| in_S .| in_M
-        else 
-            in_any = in_A .| in_S 
+        else
+            in_any = in_A .| in_S
         end
 
         if !all(in_any)

src/frontend/fit/standard_errors/hessian.jl

@@ -4,9 +4,9 @@
 Return hessian based standard errors.
 
 # Arguments
-- `hessian`: how to compute the hessian. Options are 
+- `hessian`: how to compute the hessian. Options are
     - `:analytic`: (only if an analytic hessian for the model can be computed)
-    - `:finitediff`: for finite difference approximation 
+    - `:finitediff`: for finite difference approximation
 """
 function se_hessian(sem_fit::SemFit; hessian = :finitediff)
     c = H_scaling(sem_fit.model)

src/frontend/specification/EnsembleParameterTable.jl

@@ -27,7 +27,7 @@ function Dict(partable::EnsembleParameterTable)
 end
 
 #= function DataFrame(
-        partable::ParameterTable; 
+        partable::ParameterTable;
         columns = nothing)
     if isnothing(columns) columns = keys(partable.columns) end
     out = DataFrame([key => partable.columns[key] for key in columns])

src/frontend/specification/ParameterTable.jl

@@ -224,9 +224,9 @@ update_partable!(partable::AbstractParameterTable, sem_fit::SemFit, vec, column)
 # update estimates -------------------------------------------------------------------------
 """
     update_estimate!(
-        partable::AbstractParameterTable, 
+        partable::AbstractParameterTable,
         sem_fit::SemFit)
-    
+
 Write parameter estimates from `sem_fit` to the `:estimate` column of `partable`
 """
 update_estimate!(partable::AbstractParameterTable, sem_fit::SemFit) =
@@ -236,7 +236,7 @@ update_estimate!(partable::AbstractParameterTable, sem_fit::SemFit) =
 """
     update_start!(partable::AbstractParameterTable, sem_fit::SemFit)
     update_start!(partable::AbstractParameterTable, model::AbstractSem, start_val; kwargs...)
-    
+
 Write starting values from `sem_fit` or `start_val` to the `:estimate` column of `partable`.
 
 # Arguments
@@ -262,10 +262,10 @@ end
 # update partable standard errors ----------------------------------------------------------
 """
     update_se_hessian!(
-        partable::AbstractParameterTable, 
-        sem_fit::SemFit; 
+        partable::AbstractParameterTable,
+        sem_fit::SemFit;
         hessian = :finitediff)
-    
+
 Write hessian standard errors computed for `sem_fit` to the `:se` column of `partable`
 
 # Arguments

src/observed/missing.jl

@@ -33,7 +33,7 @@ For observed data with missing values.
 - `get_data(::SemObservedMissing)` -> observed data
 - `data_rowwise(::SemObservedMissing)` -> observed data as vector per observation, with missing values deleted
 
-- `patterns(::SemObservedMissing)` -> indices of non-missing variables per missing patterns 
+- `patterns(::SemObservedMissing)` -> indices of non-missing variables per missing patterns
 - `patterns_not(::SemObservedMissing)` -> indices of missing variables per missing pattern
 - `rows(::SemObservedMissing)` -> row indices of observed data points that belong to each pattern
 - `pattern_n_obs(::SemObservedMissing)` -> number of data points per pattern

src/types.jl

@@ -147,7 +147,7 @@ Constructor for ensemble models.
 - `weights::Vector`:  Weights for each model. Defaults to the number of observed data points.
 
 All additional kwargs are passed down to the constructor for the optimizer field.
-    
+
 Returns a SemEnsemble with fields
 - `n::Int`: Number of models.
 - `sems::Tuple`: `AbstractSem`s.

test/examples/recover_parameters/recover_parameters_twofact.jl

@@ -66,7 +66,7 @@ semobserved = SemObservedData(data = x, specification = nothing)
 loss_ml = SemLoss(SEM.SemML(; observed = semobserved, n_par = length(start)))
 
 optimizer = SemOptimizerOptim(
-    BFGS(; linesearch = BackTracking(order = 3), alphaguess = InitialHagerZhang()),# m = 100), 
+    BFGS(; linesearch = BackTracking(order = 3), alphaguess = InitialHagerZhang()),# m = 100),
     Optim.Options(; f_tol = 1e-10, x_tol = 1.5e-8),
 )