Setup | RMarkdown | Quarto | Bookdown | Creating interactive widgets with inline code | Fill-In-The-Blanks | Multiple Choice | True or False | Longer MCQs | Checked sections | Hidden solutions and hints

The tidySEM workflow | Example: Graphing a CFA | Step 1: Run an analysis | Step 2: Plot the graph | Optional step 3: Customizing the layout | Automatically generate layout for a model | Specifying layout manually in R | Specifying layout in a spreadsheet program | Examples of user-defined layout | Step 4: Customize nodes and edges | Customizing node and edge labels | Customize labels of a sem_graph | Optional step 5: accessing graph data before plotting | Optional step 6: Access the nodes and edges | Optional step 7: Modify the nodes and edges | Plot the customized graph | Connecting nodes | Visual aspects | Visual aspects of edges | Visual aspects of nodes | Visual aspects of edge labels | Conditionally customize visual aspects | Math Notation in Graphs

Loading the Data | Descriptive statistics | Conducting Latent Profile Analysis | Class Enumeration | Using ICs | Using LMR tests | Using BLRT tests | Optional: Alternative Model Specifications | Interpreting the Final Class Solution | Auxiliary Analyses | Comparing Means or Proportions Across Classes | Comparing Auxiliary Models Across Classes | Predicting class membership

Loading the Data | Descriptive statistics | Conducting Latent Profile Analysis | Class Enumeration | Interpreting the Final Class Solution | Auxiliary Analyses

Loading the Data | Examining the Data | Conducting Latent Class Analysis | Class Enumeration | Using BIC | Theoretical considerations | Interpreting the Final Class Solution | Auxiliary Analyses

Data preprocessing | Class enumeration | Scree plot | Wald tests | Trajectory plot

Simulate ordinal data | Threshold syntax in as_ram() | Fit the default threshold parameterization | Testing the first threshold of X1 | Testing the second threshold of X3 | Using threshold_method = "mx_deviances" | Why the two Wald tests should agree | Reparametrizing the Model

Example Data | References

Requirements | Example Data | Model Estimation with mx_mixed_lca() | Estimating a Single Class Solution | Estimating Multiple Class Solutions | Class Enumeration | Examine Results | Advanced Options | Plotting the Model | References

Checking the Installation | Prepare a dataset using prepare_data.R | Add the Dataset to the Repository | Add some demo analyses | Reproduce the Project

Optional step

Steps Involved | Check 'Git' and 'GitHub' | Optionally: Connect Local to Remote ('GitHub') Repository | Snapshot renv Dependencies | Pushing These Changes to the Remote Repository | Check Your 'GitHub' Repository | Login to 'Zenodo' | Authorize 'GitHub' to connect with 'Zenodo' | Select the Repository to Archive | Optional: Check repository settings | Create a New Release | Verify on 'Zenodo' | Optionally: Updating Meta-Data | Verifying That 'Zenodo' Mints a DOI for Your Project | CONGRATULATIONS! | Checklist for citing your project

Install 'RStudio' and 'R' | Install R-package dependencies | Verifying WORCS Installation | Obtaining the project repository | Open the project in 'RStudio' | Restore the package dependencies | Open the project entry point | Reproduce the analyses | No access to original data

WORCS: Steps to follow for a project | Phase 1: Study design | Phase 2: Writing and analysis | Phase 3: Submission and publication | Notes for cautious researchers | Sample WORCS projects

Adding endpoints | Integration Tests as Endpoints | Adding Integration Tests | Reproducing a Project | Checking reproducibility | Updating endpoints | Automating Reproducibility | Automating Endpoint Checks

GitLab | Setup steps (do this only once) | Connect new worcs project to 'GitLab' | Bitbucket | Connect new worcs project to 'Bitbucket'

Generating Data from a Structural Equation Model | Illustrating the Problem | Adding a Custom Dataset

Defining a Pipeline | Using targets Markdown

Tutorial Requirements | Meta-Analysis in R | Using Your Own Data | Using Different Demo Data | Fixed Effect Model | Fixed-effects Model with rma | Random-Effects-Model | Random-Effects Model with rma | Meta-Regression | Testing Moderators' Significance | Assessing Meta-Regression Model Fit | Meta-regression in R | Continuous Predictors | Multiple Meta-Regression | Optional: Pitfalls of Meta-Regression | Multicollinearity | Overfitting | Penalized Meta-Regression | Two-level model | Two-level model with the lasso prior | Assessing convergence and interpreting the results | Optional: Three-level model with Horseshoe Prior | More Information | Random Forest Meta-Regression | Check Convergence | Model Tuning | Interpreting the Results | Bayesian Evidence Synthesis | Repeating the RMA | Product Bayes Factor | Credit | References

Tutorial example | Tuning parameters | Inspecting the results | More information

Output from lavaan | Output from OpenMx | Output from Mplus

Packages | Data | Impute missings | Moderators | Two-level model | Two-level model with the lasso prior | Assessing convergence and interpreting the results | Two-level model with the horseshoe prior | Three-level model | Standardization

The tidySEM workflow | Example: Running a CFA | Step 1: Check the variable names | Guidelines for naming variables | Step 2: Generate a dictionary | Step 3: Generate syntax | Step 4: Run the model | Optional step 5: Access the dictionary, data, and syntax | Optional step 6: Modify the dictionary and syntax | Optional step 7: Adding paths

Acknowledgment | Introduction | Usage | Arguments | Using bain with a lm or t_test object | Using bain with a lavaan object | Using bain with a named vector | The specification of hypotheses | Output from an analysis with bain | Examples

Nodes | Observed variables | Latent variables | Edges | Regression effects | Covariances | (Residual) variances | Basic models | Path models | Measurement model | Structural equation models

Introduction | Reference | Usage | Arguments | Output | Example analyses | Bayesian t testing | Analysis of variance | Testing independent group variances | Logistic regression | Correlation analysis | Univariate/Multivariate multiple regression | Running BF on a named vector