clean tests and docs

Author: Maximilian-Stefan-Ernst

README.md

@@ -39,7 +39,7 @@ The package makes use of
 - SparseArrays.jl to speed up symbolic computations.
 - Optim.jl and NLopt.jl to provide a range of different Optimizers/Linesearches.
 - ProximalAlgorithms.jl for regularization.
-- FiniteDiff.jl and ForwardDiff.jl to provide gradients for user-defined loss functions.
+- FiniteDiff.jl and to provide gradient approximations for user-defined loss functions.
 
 # At the moment, we are still working on:
 - optimizing performance for big models (with hundreds of parameters)

docs/src/developer/loss.md

@@ -204,7 +204,7 @@ julia>?
 
 help?> RAM
 
-help?> SemObservedCommon
+help?> SemObservedData
 ```
 
 We see that the model implied covariance matrix can be assessed as `Σ(implied)` and the observed covariance matrix as `obs_cov(observed)`.

docs/src/developer/observed.md

@@ -28,8 +28,8 @@ nsamples(observed::MyObserved) = ...
 nobserved_vars(observed::MyObserved) = ...
 ```
 
-As always, you can add additional methods for properties that implied types and loss function want to access, for example (from the `SemObservedCommon` implementation):
+As always, you can add additional methods for properties that implied types and loss function want to access, for example (from the `SemObservedData` implementation):
 
 ```julia
-obs_cov(observed::SemObservedCommon) = observed.obs_cov
+obs_cov(observed::SemObservedData) = observed.obs_cov
 ```

docs/src/internals/files.md

@@ -10,7 +10,7 @@ Source code is in the `"src"` folder:
 - `"StructuralEquationModels.jl"` defines the module and the exported objects
 - `"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).
+- The folders `"observed"`, `"implied"`, and `"loss"` 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 `fit`)
     - `"optim.jl"`: connection to the `Optim.jl` package
 - `"frontend"` contains user-facing functions

docs/src/performance/simulation.md

@@ -1,9 +1,5 @@
 # Simulation studies
 
-!!! note "Simulation study interface"
-    We are currently working on an interface for simulation studies.
-    Until we are finished with this, this page is just a collection of tips.
-
 ## Replace observed data
 In simulation studies, a common task is fitting the same model to many different datasets.
 It would be a waste of resources to reconstruct the complete model for each dataset.

docs/src/tutorials/construction/outer_constructor.md

@@ -14,9 +14,8 @@ Structural Equation Model
 - Loss Functions
    SemML
 - Fields
-   observed:  SemObservedCommon
-   implied:     RAM
-   optimizer: SemOptimizerOptim
+   observed:  SemObservedData
+   implied:   RAM
 ```
 
 The output of this call tells you exactly what model you just constructed (i.e. what the loss functions, observed, implied and optimizer parts are).