Introduction | Data | Model | Empirical Equivalence | Example 1: Model Chi-Squares | Calling eq_models() | Examine the Models | Visualize the Models | Example 2: CFI | Calling eq_models() with tolerance | Selecting Models | Final Remarks | Customize the Search | Equivalence-In-Principle and Empirical Equivalence | References

Introduction | Data | Model | Empirical Equivalence | Example 1: Model Chi-Squares | Calling eq_models() | Examine the Models | Visualize the Models | Example 2: CFI | Calling eq_models() with tolerance | Selecting Models | Final Remarks | Customize the Search | Equivalence-In-Principle and Empirical Equivalence | References

Introduction | The Workflow | Demonstration: lavaan::sem() | Fit a Model by lavaan::sem() | Using the Output of do_boot() in Other Functions of manymome | Demonstration: lm() | Fit the Model by Several Calls to lm() | The Structure of the Output | Models Fitted by lavaan::sem() | Models Fitted by lm() | Further Information | References

Introduction | How It Works | The Workflow | Demonstration | Fit a Model by lavaan::sem() | Using the Output of do_mc() in Other Functions of manymome | Missing Data | The Structure of the Output | Limitations | Further Information | Reference

Introduction | Workflow | What Will Be Covered In This Get-Started Article | Moderated Mediation by SEM using lavaan | Fitting the Model | Conditional Indirect Effects | Do Bootstrapping (Once) | Estimate Conditional Indirect Effects | Examine the Effect at a Particular Set of Levels of the Moderators | Changing the Levels of the Moderators | Standardized Conditional Indirect Effects | Index of Moderated Moderated Mediation | Index of Moderated Mediation | Mediation Only | Estimate Indirect Effects | Standardized Indirect Effect | Estimating Indirect Effects For Any Paths | Total Indirect Effects and Total Effects | Summary | Advantages | Limitations | Other Uses and Scenarios | Monte Carlo Confidence Intervals | References

Introduction | Data Set and Model | Generating Bootstrap Estimates | Indirect Effects | Standardized Indirect effects | Adding Effects | Differences in Effects | Identifying All Indirect paths | Total Indirect Effect | Further Information | Reference

Introduction | Data Set and Model | Generating Bootstrap Estimates | Indirect Effects | Standardized Indirect effects | Adding Effects | Differences in Effects | Identifying All Indirect paths | Further Information | Reference

Introduction | Data Set and Model | Generating Bootstrap Estimates | Conditional Indirect Effects | Index of Moderated Mediation | Standardized Conditional Indirect effects | More Complicated Models | Reference

Introduction | How It Works | The Workflow | Demonstration | Multiple Imputation | Fit a Model by lavaan.mi::sem.mi() | Generate Monte Carlo Estimates | Using the Output of do_mc() in Other Functions of manymome | Limitation | Further Information | Reference

Introduction | Numeric Moderators | SD and Mean | Percentiles | Specific Values | Merging the Levels of Two Or More Moderators | Different Settings For Moderators | Categorical Moderators | Create Dummy Variables and Fit the Model | Default Levels | User-Supplied Levels | Mixing Numeric and Categorical Moderators | Determining The Type of a Moderator | Further Information | Reference

Introduction | Fit a Model to a Dataset | Examples | Find the LBCI for Selected Free Parameters | Default Parameters | Customizing the Printout | Find the LBCI for a User-Defined Parameter | Find the LBCI for the Parameters in the Standardized Metric | Basic Arguments in semlbci() | sem_out and pars: The Fit Object and the Parameters | ciperc: The Level of Confidence | standardized: Whether Standardized Solution Is Used | parallel and ncpus | try_k_more_times and semlbci_out | Other Arguments | Additional Features | Multiple-Group Models | Robust LBCI | Latent Level Parameters | More Examples | Limitations | Estimators | Models | Methods | Technical Details | References

Introduction | Limitations | Fitting a Simple Mediation model | Log Profile likelihood Plots | The a-path | The indirect effect | Final Remarks | Reference

Introduction | Examples | A Simple Mediation Model | Find the LBCI of a Regression Coefficient | Find the LBCI of a Function of Coefficients: The Indirect Effect | References

Using Scores to Approximate Case Influence | Comparison | Generalized Cook's distance (gCD) | Approximate Change in Fit | Comparing exact and approximate changes in fit indices | Limitations | References

Purpose | Leave-One-Out Approach | Workflow | Fitting the Target Model | Rerun n Times (Step 1 to Step 3) | Diagnostic Functions | Standardized Changes in Parameter Estimates (DFTHETAS) | Raw Change in Parameter Estimates (DFTHETA) | Mahalanobis Distance | Changes in Fit Measures | An All-In-One-Function | Diagnostic Plots | Generalized Cook's Distance | Change in Fit Measure vs. Generalized Cook's Distance | Bubble Plot | Index Plot of Standardized Changes (DFTHETASs) or Raw Changes (DFTHETAs) in Parameter Estimates | Standardized Changes Against gCD | Approximate Approach | Approximate Standardized Changes in Parameter Estimates | Approximate Raw Changes in Parameter Estimates | Approximate Changes in Fit Measures | Approximate Generalized Cook's Distance | Approximate Change in Fit Measure vs. Approximate Generalized Cook's Distance | Index Plot of Standardized or Raw Changes in Parameter Estimates | Final Remarks | References

Rerun n Times | Diagnostic Functions | Standardized Changes in Estimates | Raw Changes in Estimates | Mahalanobis Distance | Changes in Fit Measures | All-In-One-Function | Diagnostic Plots | Generalized Cook's Distance | Fit Measure vs. Generalized Cook's Distance | Bubble Plot | References

Introduction | This Guide Shows to use std_selected() to: | Sample Dataset | Model | Correct Standardization For the Moderated Regression | The Arguments | Advantage | Nonparametric Bootstrap Confidence Intervals | Standardize Independent Variable (Focal Variable) and Moderator | Further Information | Reference(s)

Introduction | What cond_effect() Can Do | Major Arguments | Regression Output, Predictor (x), and Moderator (w) | Levels of the Moderator | Numeric Moderators | Categorical Moderators | Nonparametric Bootstrap Confidence Intervals | Further Information | Reference

Purpose | Setup the Environment | Load the Dataset | Moderated Regression | Mean Center the Moderator | Mean Center The Moderator and the Focal Variable | Standardize The Moderator and The Focal Variable | Standardize The Moderator, The Focal Variable, and the Outcome Variable | Standardize All Variables | The Usual Standardized Solution | Improved Confidence Interval For "Betas" | Further Information | Reference

Introduction | What plotmod() Can Do | Major Arguments | Model, Predictor (x), and Moderator (w) | Levels of the Moderator | Numeric Moderators | Categorical Moderators | Tumble Graph | Decoration and Annotation | Variable Labels | Title | Line Width and Point Size | Information | Conditional Effects | Definitions of the Levels of the Moderator | Any Variables Standardized? | Tweak the Graph | Technical Notes for Tumble Graph | Further Information | Reference

Purpose | Setup the Environment | Load the Dataset | Moderated Regression | Standardized Moderation Effect | The Common (Incorrect) Standardized Solution | Improved Confidence Intervals | Further Information | Reference(s)

Purpose | Setup the Environment | Load the Dataset | Fit the Model by lavaan::sem() | Compute the Standardized Moderation Effect | Form Bootstrap Confidence Interval | Remarks | Reference(s)

Introduction | Several Indirect Effects | Conditional Indirect Effects | Other Features | Further Processing by flextable | Other Options

Introduction | Mark all parameter estimates by asterisks based on p-values: mark_sig() | Add standard error estimates or confidence interval: mark_se() and mark_ci() | Rotate the residuals: rotate_resid() and safe_resid_position() | Set the curve attributes: set_curve() | Set the positions of parameters: set_edge_label_position() | Change one or more node labels: change_node_label() | Using pipe-operator | Limitations

Introduction | Data and Model | Problems With Standardization | Beta-Select by glm_betaselect() | Estimates Only | Estimates and Bootstrap Confidence Interval | Estimates and Bootstrap Confidence Intervals, With Only Selected Variables Standardized | Categorical Variables | Conclusion | References

Introduction | Data and Model | Problems With Standardization | Beta-Select by lm_betaselect() | Estimates Only | Estimates and Bootstrap Confidence Interval | Estimates and Bootstrap Confidence Intervals, With Only Selected Variables Standardized | Categorical Variables | Conclusion | References

Introduction | Data and Model | Problems With Standardization | Beta-Select lav_betaselect() | Estimates Only | Estimates and Bootstrap Confidence Interval | Estimates and Bootstrap Confidence Intervals, With Only Selected Variables Standardized | Categorical Variables | Conclusion | References

Introduction | Workflow | Example | Step 1: Fit the Original Model | Step 2: Call model_set() | Step 3: Examine the Results | Repeat Step 2 with User Prior | Advanced Options | More Neighboring Models | Excluding Some Parameters From the Search | Models With Constraints | Recompute BPPs Without Refitting the Models | Many Neighboring Models | More Options | Further Information | References

Introduction | Prerequisite | Scope | Package | Workflow | Mediation | Specify the Population Model | Specify The Population Values | Specify the Measurement Part | Call power4test() to Check the Model | Call power4test() to Do the Target Test | Compute the Power | Repeat a Simulation With A Different Sample Size | Repeat a Simulation With Different Numbers of Indicators or Reliability | Find the Sample Size With Desired Power | Using n_region_from_power() | Using power4test_by_n() | Using x_from_power() | Other Scenarios | References

Introduction | Prerequisite | Scope | Package | Workflow | Mediation | Specify the Population Model | Specify The Population Values | Call power4test() to Check the Model | Call power4test() to Do the Target Test | Compute the Power | Moderation | Specify the Population Model and Values | Call power4test() to Test The Moderation Effect | Moderated mediation | Specify the Population Model and Values | Call power4test() to Test The Moderated Mediation Effect | Repeating a Simulation With A Different Sample Size | Find the Sample Size With The Desired Power | Using n_region_from_power() | Using power4test_by_n() | Using x_from_power() | Other Advanced Features | Limitations | References

Row Numbers or Case IDs | Mahalanobis Distance on Residuals | Mahalanobis Distance on All Variables | Final Remarks | References

Note | Introduction | What standardizedSolution_boot_ci() Does | Data and Model | Bootstrap Percentile CIs for Standardized Solution | Print in a Friendly Format | Background | Reference(s)

Introduction | Manipulate Parameter Estimates Tables | Add Significance Test Results: add_sig() | Filter by Operators and Some Other Columns: filter_by() | Group By DVs or Group By IVs: group_by_dvs() and group_by_ivs() | Group By Groups: group_by_groups() | Compare Models: group_by_models() | Sort Rows: sort_by() | Piping | Compare Methods | Bootstrapping | Others | Showing the Options in a Model | Recoding Minimization History | Add Covariances Between "Exogenous" Variables

Introduction | For What Scenarios? | Scenarios | Create a List of Data frames | Insert a Column Computed From Another Column | Add a Header or Footer Section | Further Information | Issues

Introduction | LRT p-Values | Basic Workflow | Data | Model | LRT p-Values For Selected Parameters | How LRT p-Values Are Computed | Why LRT p-Value | Limitations | Further Information | References

Introduction | Hierarchical Regression Analysis | Test The Highest Order Term | Issues

Introduction | Example | Keep Or Drop? It Depends

Introduction | What layout Does in semPlot::semPaths. | layout_matrix() in semptools | factor_layout in set_sem_layout()

Introduction | The Initial semPaths Plot | Modify the Plot | Special Treatment | Example | Further information

Introduction | The Initial semPaths Graph | Order the Indicators and Factors | Change the Curvatures of the Factor Covariances | Move the Loadings | Rotate the Model | Pipe | Limitations

Introduction | The Initial semPaths Graph | Assign Indicators to Factors | Move Indicators | "Push" the indicators away | "Spread" out the indicators | Move the Loadings | Pipe | Limitations