clean docs

Author: Maximilian-Stefan-Ernst

docs/make.jl

@@ -52,7 +52,6 @@ makedocs(
             "files" => "internals/files.md",
             "types" => "internals/types.md",
         ],
-        "Complementary material" => ["Mathematical appendix" => "complementary/maths.md"],
     ],
     format = Documenter.HTML(
         prettyurls = get(ENV, "CI", nothing) == "true",

docs/src/developer/implied.md

@@ -62,11 +62,8 @@ model per group and an additional model with `ImpliedEmpty` and `SemRidge` for t
 # Extended help
 
 ## Interfaces
-- `params(::RAMSymbolic) `-> Vector of parameter labels
-- `nparams(::RAMSymbolic)` -> Number of parameters
-
-## Implementation
-Subtype of `SemImplied`.
+- `param_labels(::ImpliedEmpty) `-> Vector of parameter labels
+- `nparams(::ImpliedEmpty)` -> Number of parameters
 """
 struct ImpliedEmpty{A, B, C} <: SemImplied
     hessianeval::A
@@ -78,7 +75,12 @@ end
 ### Constructors
 ############################################################################################
 
-function ImpliedEmpty(;specification, meanstruct = NoMeanStruct(), hessianeval = ExactHessian(), kwargs...)
+function ImpliedEmpty(;
+    specification,
+    meanstruct = NoMeanStruct(),
+    hessianeval = ExactHessian(),
+    kwargs...,
+)
     return ImpliedEmpty(hessianeval, meanstruct, convert(RAMMatrices, specification))
 end
 

docs/src/developer/optimizer.md

@@ -37,13 +37,13 @@ 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 `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
+```julia
 SemOptimizerName(;
     algorithm = LBFGS(),
     options = Optim.Options(; f_tol = 1e-10, x_tol = 1.5e-8),
     kwargs...,
 ) = SemOptimizerName(algorithm, options)
-´´´
+```
 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 `fit`:

docs/src/internals/internals.md

@@ -1,3 +1,3 @@
 # Internals and Design
 
-On the following pages, we document the internals and design of the package. Those informations are no prerequisite for extending the package (as decribed in the developer documentation)!, but they may be useful and hopefully interesting.
+On the following pages, we document some technical information about the package. Those informations are no prerequisite for extending the package (as decribed in the developer documentation)!, but they may be useful.

docs/src/tutorials/backends/nlopt.md

@@ -1,7 +1,7 @@
 # Using NLopt.jl
 
 [`SemOptimizerNLopt`](@ref) implements the connection to `NLopt.jl`.
-It is only available if the `NLopt` package is loaded alongside `StructuralEquationModel.jl` in the running Julia session.
+It is only available if the `NLopt` package is loaded alongside `StructuralEquationModels.jl` in the running Julia session.
 It takes a bunch of arguments:
 
 ```julia

docs/src/tutorials/construction/build_by_parts.md

@@ -47,21 +47,21 @@ partable = ParameterTable(
 Now, we construct the different parts:
 
 ```@example build
-# observed ---------------------------------------------------------------------------------
+# observed -----------------------------------------------------------------------------
 observed = SemObservedData(specification = partable, data = data)
 
-# implied ------------------------------------------------------------------------------------
+# implied ------------------------------------------------------------------------------
 implied_ram = RAM(specification = partable)
 
-# loss -------------------------------------------------------------------------------------
+# loss ---------------------------------------------------------------------------------
 ml = SemML(observed = observed)
 
 loss_ml = SemLoss(ml)
 
-# optimizer -------------------------------------------------------------------------------------
+# optimizer ----------------------------------------------------------------------------
 optimizer = SemOptimizerOptim()
 
-# model ------------------------------------------------------------------------------------
+# model --------------------------------------------------------------------------------
 
 model_ml = Sem(observed, implied_ram, loss_ml)