Rename params

Project.toml

@@ -18,6 +18,7 @@ PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StenoGraphs = "78862bba-adae-4a83-bb4d-33c106177f81"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"

README.md

@@ -15,7 +15,7 @@ Models you can fit include
 - Multigroup SEM
 - Sums of arbitrary loss functions (everything the optimizer can handle).
 
-# What are the merrits?
+# What are the merits?
 
 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.

docs/src/developer/loss.md

@@ -79,7 +79,7 @@ model = SemFiniteDiff(
     loss = (SemML, myridge)
 )
 
-model_fit = sem_fit(model)
+model_fit = fit(model)
 ```
 
 This is one way of specifying the model - we now have **one model** with **multiple loss functions**. Because we did not provide a gradient for `Ridge`, we have to specify a `SemFiniteDiff` model that computes numerical gradients with finite difference approximation.
@@ -117,17 +117,17 @@ model_new = Sem(
     loss = (SemML, myridge)
 )
 
-model_fit = sem_fit(model_new)
+model_fit = fit(model_new)
 ```
 
 The results are the same, but we can verify that the computational costs are way lower (for this, the julia package `BenchmarkTools` has to be installed):
 
 ```julia
 using BenchmarkTools
 
-@benchmark sem_fit(model)
+@benchmark fit(model)
 
-@benchmark sem_fit(model_new)
+@benchmark fit(model_new)
 ```
 
 The exact results of those benchmarks are of course highly depended an your system (processor, RAM, etc.), but you should see that the median computation time with analytical gradients drops to about 5% of the computation without analytical gradients.
@@ -241,7 +241,7 @@ model_ml = SemFiniteDiff(
     loss = MaximumLikelihood()
 )
 
-model_fit = sem_fit(model_ml)
+model_fit = 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.

docs/src/developer/optimizer.md

@@ -34,7 +34,7 @@ algorithm(optimizer::SemOptimizerName) = optimizer.algorithm
 options(optimizer::SemOptimizerName) = optimizer.options
 ```
 
-Note that your optimizer is a subtype of `SemOptimizer{:Name}`, where you can choose a `:Name` that can later be used as a keyword argument to `sem_fit(engine = :Name)`.
+Note that your optimizer is a subtype of `SemOptimizer{:Name}`, where you can choose a `:Name` that can later be used as a keyword argument to `fit(engine = :Name)`.
 Similarly, `SemOptimizer{:Name}(args...; kwargs...) = SemOptimizerName(args...; kwargs...)` should be defined as well as a constructor that uses only keyword arguments:
 
 ´´´julia
@@ -46,10 +46,10 @@ SemOptimizerName(;
 ´´´
 A method for `update_observed` and additional methods might be usefull, but are not necessary.
 
-Now comes the substantive part: We need to provide a method for `sem_fit`:
+Now comes the substantive part: We need to provide a method for `fit`:
 
 ```julia
-function sem_fit(
+function fit(
     optim::SemOptimizerName,
     model::AbstractSem,
     start_params::AbstractVector;

docs/src/internals/files.md

@@ -11,7 +11,7 @@ Source code is in the `"src"` folder:
 - `"types.jl"` defines all abstract types and the basic type hierarchy
 - `"objective_gradient_hessian.jl"` contains methods for computing objective, gradient and hessian values for different model types as well as generic fallback methods
 - The four folders `"observed"`, `"implied"`, `"loss"` and `"diff"` contain implementations of specific subtypes (for example, the `"loss"` folder contains a file `"ML.jl"` that implements the `SemML` loss function).
-- `"optimizer"` contains connections to different optimization backends (aka methods for `sem_fit`)
+- `"optimizer"` contains connections to different optimization backends (aka methods for `fit`)
     - `"optim.jl"`: connection to the `Optim.jl` package
 - `"frontend"` contains user-facing functions
     - `"specification"` contains functionality for model specification

docs/src/performance/mixed_differentiation.md

@@ -19,15 +19,15 @@ model_ridge = SemFiniteDiff(
 
 model_ml_ridge = SemEnsemble(model_ml, model_ridge)
 
-model_ml_ridge_fit = sem_fit(model_ml_ridge)
+model_ml_ridge_fit = fit(model_ml_ridge)
 ```
 
 The results of both methods will be the same, but we can verify that the computation costs differ (the package `BenchmarkTools` has to be installed for this):
 
 ```julia
 using BenchmarkTools
 
-@benchmark sem_fit(model)
+@benchmark fit(model)
 
-@benchmark sem_fit(model_ml_ridge)
+@benchmark fit(model_ml_ridge)
 ```

docs/src/performance/mkl.md

@@ -27,9 +27,9 @@ To check the performance implications for fitting a SEM, you can use the [`Bench
 ```julia
 using BenchmarkTools
 
-@benchmark sem_fit($your_model)
+@benchmark fit($your_model)
 
 using MKL
 
-@benchmark sem_fit($your_model)
+@benchmark fit($your_model)
 ```

docs/src/performance/simulation.md

@@ -100,7 +100,7 @@ models = [model1, model2]
 fits = Vector{SemFit}(undef, 2)
 
 Threads.@threads for i in 1:2
-    fits[i] = sem_fit(models[i])
+    fits[i] = fit(models[i])
 end
 ```
 

docs/src/performance/starting_values.md

@@ -1,9 +1,9 @@
 # Starting values
 
-The `sem_fit` function has a keyword argument that takes either a vector of starting values or a function that takes a model as input to compute starting values. Current options are `start_fabin3` for fabin 3 starting values [^Hägglund82] or `start_simple` for simple starting values. Additional keyword arguments to `sem_fit` are passed to the starting value function. For example,
+The `fit` function has a keyword argument that takes either a vector of starting values or a function that takes a model as input to compute starting values. Current options are `start_fabin3` for fabin 3 starting values [^Hägglund82] or `start_simple` for simple starting values. Additional keyword arguments to `fit` are passed to the starting value function. For example,
 
 ```julia
-    sem_fit(
+    fit(
         model; 
         start_val = start_simple,
         start_covariances_latent = 0.5

docs/src/tutorials/collection/multigroup.md

@@ -81,7 +81,7 @@ model_ml_multigroup = SemEnsemble(
 We now fit the model and inspect the parameter estimates:
 
 ```@example mg; ansicolor = true
-fit = sem_fit(model_ml_multigroup)
+fit = fit(model_ml_multigroup)
 update_estimate!(partable, fit)
 details(partable)
 ```

docs/src/tutorials/constraints/constraints.md

@@ -48,7 +48,7 @@ model = Sem(
     data = data
 )
 
-model_fit = sem_fit(model)
+model_fit = fit(model)
 
 update_estimate!(partable, model_fit)
 
@@ -153,7 +153,7 @@ model_constrained = Sem(
     data = data
 )
 
-model_fit_constrained = sem_fit(constrained_optimizer, model_constrained)
+model_fit_constrained = fit(constrained_optimizer, model_constrained)
 ```
 
 As you can see, the optimizer converged (`:XTOL_REACHED`) and investigating the solution yields
@@ -162,7 +162,7 @@ As you can see, the optimizer converged (`:XTOL_REACHED`) and investigating the
 update_partable!(
     partable,
     :estimate_constr,
-    params(model_fit_constrained), 
+    param_labels(model_fit_constrained), 
     solution(model_fit_constrained), 
     )
 

docs/src/tutorials/construction/build_by_parts.md

@@ -65,5 +65,5 @@ optimizer = SemOptimizerOptim()
 
 model_ml = Sem(observed, implied_ram, loss_ml)
 
-sem_fit(optimizer, model_ml)
+fit(optimizer, model_ml)
 ```

docs/src/tutorials/construction/outer_constructor.md

@@ -131,4 +131,4 @@ model = SemFiniteDiff(
 )
 ```
 
-constructs a model that will use finite difference approximation if you estimate the parameters via `sem_fit(model)`.
+constructs a model that will use finite difference approximation if you estimate the parameters via `fit(model)`.

docs/src/tutorials/first_model.md

@@ -110,7 +110,7 @@ model = Sem(
 We can now fit the model via
 
 ```@example high_level; ansicolor = true
-model_fit = sem_fit(model)
+model_fit = fit(model)
 ```
 
 and compute fit measures as

docs/src/tutorials/fitting/fitting.md

@@ -3,7 +3,7 @@
 As we saw in [A first model](@ref), after you have build a model, you can fit it via
 
 ```julia
-model_fit = sem_fit(model)
+model_fit = fit(model)
 
 # output
 
@@ -45,30 +45,30 @@ Structural Equation Model
 
 ## Choosing an optimizer
 
-To choose a different optimizer, you can call `sem_fit` with the keyword argument `engine = ...`, and pass additional keyword arguments:
+To choose a different optimizer, you can call `fit` with the keyword argument `engine = ...`, and pass additional keyword arguments:
 
 ```julia
 using Optim
 
-model_fit = sem_fit(model; engine = :Optim, algorithm = BFGS())
+model_fit = fit(model; engine = :Optim, algorithm = BFGS())
 ```
 
 Available options for engine are `:Optim`, `:NLopt` and `:Proximal`, where `:NLopt` and `:Proximal` are only available if the `NLopt.jl` and `ProximalAlgorithms.jl` packages are loaded respectively.
 
 The available keyword arguments are listed in the sections [Using Optim.jl](@ref), [Using NLopt.jl](@ref) and [Regularization](@ref).
 
-Alternative, you can also explicitely define a `SemOptimizer` and pass it as the first argument to `sem_fit`:
+Alternative, you can also explicitely define a `SemOptimizer` and pass it as the first argument to `fit`:
 
 ```julia
 my_optimizer = SemOptimizerOptim(algorithm = BFGS())
 
-sem_fit(my_optimizer, model)
+fit(my_optimizer, model)
 ```
 
 You may also optionally specify [Starting values](@ref).
 
 # API - model fitting
 
 ```@docs
-sem_fit
+fit
 ```

docs/src/tutorials/inspection/inspection.md

@@ -42,13 +42,13 @@ model = Sem(
     data = data
 )
 
-model_fit = sem_fit(model)
+model_fit = fit(model)
 ```
 
 After you fitted a model,
 
 ```julia
-model_fit = sem_fit(model)
+model_fit = fit(model)
 ```
 
 you end up with an object of type [`SemFit`](@ref).
@@ -87,8 +87,8 @@ We can also update the `ParameterTable` object with other information via [`upda
 se_bs = se_bootstrap(model_fit; n_boot = 20)
 se_he = se_hessian(model_fit)
 
-update_partable!(partable, :se_hessian, params(model_fit), se_he)
-update_partable!(partable, :se_bootstrap, params(model_fit), se_bs)
+update_partable!(partable, :se_hessian, param_labels(model_fit), se_he)
+update_partable!(partable, :se_bootstrap, param_labels(model_fit), se_bs)
 
 details(partable)
 ```
@@ -126,11 +126,11 @@ fit_measures
 AIC
 BIC
 χ²
-df
+dof
 minus2ll
 nobserved_vars
 nsamples
-params
+param_labels
 nparams
 p_value
 RMSEA

docs/src/tutorials/meanstructure.md

@@ -96,7 +96,7 @@ model = Sem(
     meanstructure = true
 )
 
-sem_fit(model)
+fit(model)
 ```
 
 If we build the model by parts, we have to pass the `meanstructure = true` argument to every part that requires it (when in doubt, simply consult the documentation for the respective part).
@@ -112,5 +112,5 @@ ml = SemML(observed = observed, meanstructure = true)
 
 model = Sem(observed, implied_ram, SemLoss(ml))
 
-sem_fit(model)
+fit(model)
 ```

docs/src/tutorials/regularization/regularization.md

@@ -120,25 +120,25 @@ Let's fit the regularized model
 
 ```@example reg
 
-fit_lasso = sem_fit(optimizer_lasso, model_lasso)
+fit_lasso = fit(optimizer_lasso, model_lasso)
 ```
 
 and compare the solution to unregularizted estimates:
 
 ```@example reg
-fit = sem_fit(model)
+fit = fit(model)
 
 update_estimate!(partable, fit)
 
-update_partable!(partable, :estimate_lasso, params(fit_lasso), solution(fit_lasso))
+update_partable!(partable, :estimate_lasso, param_labels(fit_lasso), solution(fit_lasso))
 
 details(partable)
 ```
 
-Instead of explicitely defining a `SemOptimizerProximal` object, you can also pass `engine = :Proximal` and additional keyword arguments to `sem_fit`:
+Instead of explicitely defining a `SemOptimizerProximal` object, you can also pass `engine = :Proximal` and additional keyword arguments to `fit`:
 
 ```@example reg
-fit = sem_fit(model; engine = :Proximal, operator_g = NormL1(λ))
+fit = fit(model; engine = :Proximal, operator_g = NormL1(λ))
 ```
 
 ## Second example - mixed l1 and l0 regularization
@@ -162,13 +162,13 @@ model_mixed = Sem(
     data = data,    
 )
 
-fit_mixed = sem_fit(model_mixed; engine = :Proximal, operator_g = prox_operator)
+fit_mixed = fit(model_mixed; engine = :Proximal, operator_g = prox_operator)
 ```
 
 Let's again compare the different results:
 
 ```@example reg
-update_partable!(partable, :estimate_mixed, params(fit_mixed), solution(fit_mixed))
+update_partable!(partable, :estimate_mixed, param_labels(fit_mixed), solution(fit_mixed))
 
 details(partable)
 ```

docs/src/tutorials/specification/ram_matrices.md

@@ -59,7 +59,7 @@ spec = RAMMatrices(;
     A = A, 
     S = S, 
     F = F, 
-    params = θ,
+    param_labels = θ,
     vars = [:x1, :x2, :x3, :y1, :y2, :y3, :y4, :y5, :y6, :y7, :y8, :ind60, :dem60, :dem65]
 )
 
@@ -90,7 +90,7 @@ spec = RAMMatrices(;
     A = A, 
     S = S, 
     F = F, 
-    params = θ,
+    param_labels = θ,
     vars = [:x1, :x2, :x3, :y1, :y2, :y3, :y4, :y5, :y6, :y7, :y8, :ind60, :dem60, :dem65]
 )
 ```

ext/SEMNLOptExt/NLopt.jl

@@ -71,8 +71,8 @@ function SemFit_NLopt(optimization_result, model::AbstractSem, start_val, opt)
     )
 end
 
-# sem_fit method
-function SEM.sem_fit(
+# fit method
+function SEM.fit(
     optim::SemOptimizerNLopt,
     model::AbstractSem,
     start_params::AbstractVector;

ext/SEMProximalOptExt/ProximalAlgorithms.jl

@@ -40,7 +40,7 @@ mutable struct ProximalResult
     result::Any
 end
 
-function SEM.sem_fit(
+function SEM.fit(
     optim::SemOptimizerProximal,
     model::AbstractSem,
     start_params::AbstractVector;