Introduction | Typical Workflow | Step 1: Prepare Your Data | Step 2: Compute Naive Learning Estimate | Step 3: Fit the LCA Model | Step 4: Extract and Interpret Results | Step 5: Get Standard Errors via Bootstrap | Step 6: Assess Model Fit | Step 7: Compare Across Groups (Optional) | The Latent Class Model | Three Latent Classes | Cell Probability Derivation | Worked Example | Identification | Implementation Verification | Parameter Recovery Demonstration | Monte Carlo Validation | Bias Assessment | Standard Error Assessment | Coverage Assessment | Visualization | Sample Size Effects | DK Model Extension | Using validate_recovery() | Individual-Level Learning Recovery | The Key Insight | Computing Posterior Class Probabilities | Comparison with Cross-Sectional IRT | Monte Carlo Comparison | Effect of Gamma (Guessing Rate) | Interpreting Posterior Probabilities | Conclusion | References | Session Info

guess: Adjust Estimates of Learning for Guessing | Measuring Learning: | Estimand | Other Issues | Standard Correction for Guessing | Latent Class Correction for Guessing | Installation | Usage | Transition Matrix | Adjusting Using the Latent Class Model | Adjust by Groups | Standard Errors | Fit | Model Criticism: Perplexity and Cross-Validation | Perplexity from aggregated data (items) | Perplexity from individual data | Cross-validation over items | Cross-validation over individuals | Model comparison | Simulation and Parameter Recovery Validation | Basic simulation (No-DK model) | Simulation with Don't Know responses | Comprehensive parameter recovery validation | Statistical Efficiency: Sample Size and Parameter Recovery | Sample Size Effect | Number of Items Effect | Parameter Scenarios | Why Individual-Level Data Provides Efficiency Gains