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This is a package for Structural Equation Modeling. It is still in development. Models you can fit include
- Linear SEM that can be specified in RAM (or LISREL) notation
- ML, GLS and FIML estimation
- Regularization
- Multigroup SEM
- Sums of arbitrary loss functions (everything the optimizer can handle).
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 want to extend SEM (e.g. add a new objective function) and need an extensible 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)
- you are planning a study and would like to do power simulations
The package makes use of
- Symbolics.jl for symbolically precomputing parts of the objective and gradients to generate fast, specialized functions.
- SparseArrays.jl to speed up symbolic computations.
- 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.
- optimizing performance for big models (with hundreds of parameters)
If you have questions you may ask them here in the issues. Please observe our code of conduct.