The random utility model in two spaces | References

Basic required format | Continuous versus discrete variables | Creating dummy coded variables | Validating data before estimation | Basic validation | Validation with parameters | Panel data validation | Common errors detected

What Are Priors? | Sources of Prior Information | Basic Prior Specification | Fixed Parameters | Understanding Categorical Variables | Using Named Specifications | Random Parameters | Parameter Correlations | Types of Correlations | Interaction Effects | Interaction Types | No-Choice Priors | Suggested Priors | Parameter Draws for Bayesian Analysis | Common Pitfalls | Mismatched Scales | Wrong Reference Levels | Incompatible Restrictions | Using Priors in Practice

Design Basics | Understanding the Design Structure | ID Columns | Attribute Columns | Converting Encoding | Design Methods | Method Comparison Table | "random" Method | Frequency-Based Methods | D-Optimal Methods | Comparing Designs | Design Features | No-Choice Option | Labeled Designs | Balanced Sampling | Blocking | Dominance Removal | Interactions | Comprehensive Design Inspection | Customizing Optimization | Next Steps

The cbcTools Workflow | Step 1: Generate Profiles | Step 2: Specify Priors | Understanding Reference Levels | Step 3: Generate Designs | Step 4: Inspect Design | Step 5: Simulate Choices | Step 6: Assess Power

Understanding Power Analysis | What is Statistical Power? | Why Conduct Power Analysis? | Power vs. Precision | Basic Power Analysis | Parameter Specification Options | Auto-Detection (Recommended) | Specify Dummy-Coded Parameters | When to Use Each Approach | Understanding Power Results | Visualizing Power Curves | Interpreting Results | Mixed Logit Models | Comparing Design Performance | Design Method Comparison | Advanced Analysis | Returning Full Models | Best Practices | Power Analysis Workflow

Choice Simulation Approaches | Random Choices | Utility-Based Choices | Choice Data Format | Advanced Simulation Options | Designs with No-Choice | Random Parameters (Mixed Logit) | Interaction Effects | Validating Choice Patterns | Overall Choice Frequencies | Respondent Heterogeneity | Design Consistency | Using Consistent Priors | Prior Consistency Warnings | Best Practices | Prior Specification | Validation Steps | Next Steps

Overview | Basic Encoding Conversion | Creating a Design | Converting to Dummy Coding | Converting to Effects Coding | Converting Back to Standard | Customizing Reference Levels | Setting Custom References | Updating References Without Changing Encoding | Working with No-Choice Options | Use Cases | For Model Estimation | For Data Inspection | For Power Analysis

Generating all possible profiles | Restricted profiles

What is D-error? | Types of D-error | Prior Parameter Assumptions | How cbcTools Chooses D-error Type | Working Example | The Design | Step 1: Encode the Design | Computing $D_p$-error (Fixed Parameters) | Step 2: Compute Choice Probabilities | Step 3: Compute Fisher Information Matrix | Step 4: Compute $D_p$-error | Computing $D_0$-error (No Priors) | Computing $D_B$-error (Random Parameters)

Setup | Basic settings | Package-specific settings | Prep for | Prep for | Prep for | Estimate WTP space models

Predicting probabilities | Preference space models | WTP space models | Predicting outcomes | References

Data format | Model specification interface | Parallelized multi-start estimation | Mixed logit models | Viewing results | Computing and comparing WTP | Predicting probabilities and outcomes

Supported distributions | The data | Preference space model | WTP space model | Correlated heterogeneity | References

Extracting summary tables | The {broom} package | Extracting other values | Formatted summary tables | The {gtsummary} package | The {texreg} package

Comparing run times

The data | Preference space model | WTP space model | Compare WTP from both models | References

The data | The utility model | Unweighted model | Weighted model | Compare results | References

Interactions with continuous variables | Interactions with discrete variables | Interactions with individual-specific variables | Interactions in mixed logit models