Motivations for using tablespan | Example: creating a tablespan object | Styling with tablespan | Column style with a color scale | Exporting tables | Export to Excel (openxlsx) | Supported formats | Example | Export to gt and further refinement | Export to flextable | Export to huxtable

First Step: Setting up a Multi-Group Model | Second Step: Pass the Model to lessSEM | Different Models with Shared Parameter Labels | Regularized Multi-Group Models | Regularizing Differences Between Parameters using lessSEM | Cross-Validation | Definition Variables | The details ... | Bibliography

The example | The first approach: Interfacing from R | The second approach: Using C++ function pointers | 1. Creating a fitting function and a gradient function | 2. Adapting the functions to the constraints | Step 3: Creating pointers to our functions | Optimizing the model | The third and fourth approach: Including the header files

Regularized Structural Equation Modeling | Objectives | Regularizing SEM | Setting up a model | Selecting a model | Cross-Validation | Missing Data | Using multiple cores | Changing the optimizer | Parameter transformations | Experimental Features | From lessSEM to lavaan | Multi-Group Models and Definition Variables | Mixed Penalties | More information | References | R - Packages / Software | Penalty Functions | Optimizer | GLMNET | Variants of ISTA | LICENSE NOTE

Getting Started | Using ista | Bibliography

Motivation | Using Regularization | Setting up the Model | Some Guidelines | Further Examples | Looking under the hood | Making use of C++ | How it is implemented | Bibliography

From lavaan to lessSEM | Working with the Rcpp_SEMCpp class | Accessing the Parameters | Changing the Parameters | Fitting the model | Computing the gradients | Computing the Hessian | Computing the Scores | Using lessSEM with general purpose optimizers | References

Model specification | Fitting the model | Plotting Individual Parameters | References

Person-Specific Occasions | Bibliography

Loadings, Regressions, and Intercepts | Parameter labels and constraints | Bounds | (Non-)linear constraints | Definition variables | Model name | Starting Values | References

Maximum Likelihood Estimation | Missing data | Weighted Least Squares | Ordered data | Bibliography

Ableitung der Log-Likelihood | Element 1 | Element 2 | Fall 1: Der Parameter $\theta_j$ ist in $\pmb S$. | Fall 2: Der Parameter $\theta_j$ ist in $\pmb A$. | Fall 3: Der Parameter $\theta_j$ ist in $\pmb m$, wobei $\pmb m$ die Mittelwertstruktur des SEM ist. | Element 3 | Fall 3: Der Parameter $\theta_j$ ist in $\pmb m$.

SCAD | Assume: $|x| \leq \lambda$ | Assume: $\lambda < |x| \leq \lambda\theta$ | Assume: $|x| \geq \theta\lambda$ | combining the solutions | MCP | Assume: $|x| \leq \theta\lambda$ | Assume that the solution is given by $x \geq 0$. | Assume that the solution is given by $x \leq 0$. | Assume $|x| \geq \theta\lambda$ | References