Release/v0 2 0

.github/workflows/CI.yml

@@ -15,7 +15,7 @@ jobs:
       fail-fast: true
       matrix:
         version:
-          - '1.7.3'
+          - '1'
         os:
           - ubuntu-latest
         arch:

.github/workflows/CI_ecosystem.yml

@@ -18,7 +18,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.7.3'
+          - '1'
+          - '1.9'
         os:
           - ubuntu-latest
           - macos-latest
@@ -42,4 +43,4 @@ jobs:
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v2
         with:
-          file: lcov.info
+          file: lcov.info

.github/workflows/CI_extended.yml

@@ -18,7 +18,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.7.3'
+          - '1'
+          - '1.9'
         os:
           - ubuntu-latest
           - macos-latest
@@ -42,4 +43,4 @@ jobs:
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v2
         with:
-          file: lcov.info
+          file: lcov.info

.github/workflows/documentation.yml

@@ -17,7 +17,7 @@ jobs:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@latest
         with:
-          version: '1.7.2'
+          version: '1'
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy

.github/workflows/preview-documentation.yml

@@ -16,7 +16,7 @@ jobs:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@latest
         with:
-          version: '1.6'
+          version: '1'
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy
@@ -36,4 +36,4 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo,
               body: 'Docs can be previewed here: ${{ env.GHP_URL }}previews/PR${{ env.PR_NUMBER }}'
-            })
+            })

Project.toml

@@ -1,7 +1,7 @@
 name = "StructuralEquationModels"
 uuid = "383ca8c5-e4ff-4104-b0a9-f7b279deed53"
 authors = ["Maximilian Ernst", "Aaron Peikert"]
-version = "0.1.0"
+version = "0.2.0"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
@@ -24,7 +24,7 @@ StenoGraphs = "78862bba-adae-4a83-bb4d-33c106177f81"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
-julia = "1.7"
+julia = "1.9"
 StenoGraphs = "0.2"
 DataFrames = "1"
 Distributions = "0.25"

README.md

@@ -4,6 +4,7 @@
 |:-------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|
 | [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://structuralequationmodels.github.io/StructuralEquationModels.jl/) [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://structuralequationmodels.github.io/StructuralEquationModels.jl/dev/) | [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Github Action CI](https://github.com/StructuralEquationModels/StructuralEquationModels.jl/workflows/CI_extended/badge.svg)](https://github.com/StructuralEquationModels/StructuralEquationModels.jl/actions/) [![codecov](https://codecov.io/gh/StructuralEquationModels/StructuralEquationModels.jl/branch/main/graph/badge.svg?token=P2kjzpvM4V)](https://codecov.io/gh/StructuralEquationModels/StructuralEquationModels.jl) | [![DOI](https://zenodo.org/badge/228649704.svg)](https://zenodo.org/badge/latestdoi/228649704) |
 
+# What is this Package for?
 
 This is a package for Structural Equation Modeling.
 It is still *in development*.
@@ -14,14 +15,17 @@ Models you can fit include
 - Multigroup SEM
 - Sums of arbitrary loss functions (everything the optimizer can handle).
 
+# What are the merrits?
+
 We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof.
 As a user, you can easily define custom loss functions.
 For those, you can decide to provide analytical gradients or use finite difference approximation / automatic differentiation.
 You can choose to mix and match loss functions natively found in this package and those you provide.
 In such cases, you optimize over a sum of different objectives (e.g. ML + Ridge).
 This mix and match strategy also applies to gradients, where you may supply analytic gradients or opt for automatic differentiation or mix analytical and automatic differentiation.
 
-You may consider using this package if:
+# You may consider using this package if:
+
 - you want to extend SEM (e.g. add a new objective function) and need an extendable framework
 - you want to extend SEM, and your implementation needs to be fast (because you want to do a simulation, for example)
 - you want to fit the same model(s) to many datasets (bootstrapping, simulation studies)
@@ -33,7 +37,7 @@ The package makes use of
 - Optim.jl and NLopt.jl to provide a range of different Optimizers/Linesearches.
 - FiniteDiff.jl and ForwardDiff.jl to provide gradients for user-defined loss functions.
 
-At the moment, we are still working on
+# At the moment, we are still working on:
 - optimizing performance for big models (with hundreds of parameters)
 
 # Questions?

docs/src/developer/loss.md

@@ -217,7 +217,7 @@ Let's make a sligtly more complicated example: we will reimplement maximum likel
 To keep it simple, we only cover models without a meanstructure. The maximum likelihood objective is defined as
 
 ```math
-F_{ML} = \log \det \Sigma_i + \mathrm{tr}(\Sigma_i \Sigma_o)
+F_{ML} = \log \det \Sigma_i + \mathrm{tr}\left(\Sigma_{i}^{-1} \Sigma_o \right)
 ```
 
 where ``\Sigma_i`` is the model implied covariance matrix and ``\Sigma_o`` is the observed covariance matrix. We can query the model implied covariance matrix from the `imply` par of our model, and the observed covariance matrix from the `observed` path of our model.
@@ -269,4 +269,4 @@ model_ml = SemFiniteDiff(
 model_fit = sem_fit(model_ml)
 ```
 
-If you want to differentiate your own loss functions via automatic differentiation, check out the [AutoDiffSEM](https://github.com/StructuralEquationModels/AutoDiffSEM) package (spoiler allert: it's really easy).
+If you want to differentiate your own loss functions via automatic differentiation, check out the [AutoDiffSEM](https://github.com/StructuralEquationModels/AutoDiffSEM) package (spoiler allert: it's really easy).

src/StructuralEquationModels.jl

@@ -90,6 +90,7 @@ export  AbstractSem,
             SemConstant, SemWLS, loss,
         SemOptimizer, 
             SemOptimizerEmpty, SemOptimizerOptim, SemOptimizerNLopt, NLoptConstraint,
+            optimizer, n_iterations, convergence,
         SemObserved, 
             SemObservedData, SemObservedCovariance, SemObservedMissing, observed,
         sem_fit, 

src/frontend/fit/standard_errors/hessian.jl

@@ -26,7 +26,7 @@ function se_hessian(sem_fit::SemFit; hessian = :finitediff)
     elseif hessian == :expected
         throw(ArgumentError("standard errors based on the expected hessian are not implemented yet"))
     else
-        throw(ArgumentError("I dont know how to compute `$how` standard-errors"))
+        throw(ArgumentError("I dont know how to compute `$hessian` standard-errors"))
     end
 
     invH = c*inv(H)
@@ -44,9 +44,14 @@ H_scaling(model::AbstractSemSingle) =
         model.optimizer,
         model.loss.functions...)
 
-H_scaling(model, obs, imp, optimizer, lossfun::Union{SemML, SemWLS}) =
+H_scaling(model, obs, imp, optimizer, lossfun::SemML) =
     2/(n_obs(model)-1)
 
+function H_scaling(model, obs, imp, optimizer, lossfun::SemWLS)
+    @warn "Standard errors for WLS are only correct if a GLS weight matrix (the default) is used."
+    return 2/(n_obs(model)-1)
+end
+
 H_scaling(model, obs, imp, optimizer, lossfun::SemFIML) =
     2/n_obs(model)
 

src/frontend/specification/EnsembleParameterTable.jl

@@ -69,7 +69,22 @@ end
 
 # Sorting ----------------------------------------------------------------------------------
 
-# todo
+# Sorting ----------------------------------------------------------------------------------
+
+function sort!(ensemble_partable::EnsembleParameterTable)
+
+    for partable in values(ensemble_partable.tables)
+        sort!(partable)
+    end
+
+    return ensemble_partable
+end
+
+function sort(partable::EnsembleParameterTable)
+    new_partable = deepcopy(partable)
+    sort!(new_partable)
+    return new_partable
+end
 
 # add a row --------------------------------------------------------------------------------
 

src/imply/RAM/symbolic.jl

@@ -238,7 +238,7 @@ identifier(imply::RAMSymbolic) = imply.identifier
 n_par(imply::RAMSymbolic) = imply.n_par
 
 function update_observed(imply::RAMSymbolic, observed::SemObserved; kwargs...) 
-    if n_man(observed) == size(imply.Σ)
+    if Int(n_man(observed)) == size(imply.Σ, 1)
         return imply
     else
         return RAMSymbolic(;observed = observed, kwargs...)

src/imply/empty.jl

@@ -3,6 +3,7 @@
 ############################################################################################
 """
 Empty placeholder for models that don't need an imply part.
+(For example, models that only regularize parameters.)
 
 # Constructor
 
@@ -11,6 +12,10 @@ Empty placeholder for models that don't need an imply part.
 # Arguments
 - `specification`: either a `RAMMatrices` or `ParameterTable` object
 
+# Examples
+A multigroup model with ridge regularization could be specified as a `SemEnsemble` with one
+model per group and an additional model with `ImplyEmpty` and `SemRidge` for the regularization part.
+
 # Extended help
 
 ## Interfaces

test/examples/multigroup/build_models.jl

@@ -16,10 +16,8 @@ model_g2 = Sem(
 
 model_ml_multigroup = SemEnsemble(model_g1, model_g2; optimizer = semoptimizer)
 
-############################################################################################
-### test gradients
-############################################################################################
 
+# gradients
 @testset "ml_gradients_multigroup" begin
     @test test_gradient(model_ml_multigroup, start_test; atol = 1e-9)
 end
@@ -49,6 +47,69 @@ end
         lav_groups = Dict(:Pasteur => 1, :Grant_White => 2))
 end
 
+############################################################################################
+# ML estimation - sorted
+############################################################################################
+
+partable_s = sort(partable)
+
+specification_s = RAMMatrices(partable_s)
+
+specification_g1_s = specification_s[:Pasteur]
+specification_g2_s = specification_s[:Grant_White]
+
+model_g1 = Sem(
+    specification = specification_g1_s,
+    data = dat_g1,
+    imply = RAMSymbolic
+)
+
+model_g2 = Sem(
+    specification = specification_g2_s,
+    data = dat_g2,
+    imply = RAM
+)
+
+model_ml_multigroup = SemEnsemble(model_g1, model_g2; optimizer = semoptimizer)
+
+# gradients
+@testset "ml_gradients_multigroup | sorted" begin
+    @test test_gradient(model_ml_multigroup, start_test; atol = 1e-2)
+end
+
+grad = similar(start_test)
+gradient!(grad, model_ml_multigroup, rand(36))
+grad_fd = FiniteDiff.finite_difference_gradient(x -> objective!(model_ml_multigroup, x), start_test)
+
+# fit
+@testset "ml_solution_multigroup | sorted" begin
+    solution = sem_fit(model_ml_multigroup)
+    update_estimate!(partable_s, solution)
+    @test compare_estimates(
+        partable, 
+        solution_lav[:parameter_estimates_ml]; atol = 1e-4,
+        lav_groups = Dict(:Pasteur => 1, :Grant_White => 2))
+end
+
+@testset "fitmeasures/se_ml | sorted" begin
+    solution_ml = sem_fit(model_ml_multigroup)
+    @test all(test_fitmeasures(
+        fit_measures(solution_ml), 
+        solution_lav[:fitmeasures_ml]; rtol = 1e-2, atol = 1e-7))
+
+    update_partable!(
+        partable_s, identifier(model_ml_multigroup), se_hessian(solution_ml), :se)
+    @test compare_estimates(
+        partable_s, 
+        solution_lav[:parameter_estimates_ml]; atol = 1e-3, 
+        col = :se, lav_col = :se,
+        lav_groups = Dict(:Pasteur => 1, :Grant_White => 2))
+end
+
+@testset "sorted | LowerTriangular A" begin
+    @test imply(model_ml_multigroup.sems[2]).A isa LowerTriangular
+end
+
 ############################################################################################
 # ML estimation - user defined loss function
 ############################################################################################

test/examples/multigroup/multigroup.jl

@@ -1,5 +1,5 @@
 using StructuralEquationModels, Test, FiniteDiff
-import LinearAlgebra: diagind
+import LinearAlgebra: diagind, LowerTriangular
 # import StructuralEquationModels as SEM
 include(
     joinpath(chop(dirname(pathof(StructuralEquationModels)), tail = 3), 

test/examples/political_democracy/constraints.jl

@@ -61,6 +61,6 @@ end
     solution_constrained = sem_fit(model_ml_constrained)
     @test solution_constrained.solution[31]*solution_constrained.solution[30] >= 0.6
     @test all(abs.(solution_constrained.solution) .< 10)
-    @test_skip solution_constrained.optimization_result.result[3] == :FTOL_REACHED
+    @test solution_constrained.optimization_result.result[3] == :FTOL_REACHED skip=true
     @test abs(solution_constrained.minimum - 21.21) < 0.01
 end