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"has_valid_face" ] }, { "page": "homog_diag", "title": "Master function: Homogeneity of diagnosticity ratio", "topics": [ "homog_diag" ] }, { "page": "homog_diag_boot", "title": "Homogeneity of diagnosticity ratio with bootstrapped CIs", "topics": [ "homog_diag_boot" ] }, { "page": "i_esize_T", "title": "I Component of Effective Size (Tredoux, 1998)", "topics": [ "i_esize_T" ] }, { "page": "innocent_id_rate_effective", "title": "Compute Innocent Suspect ID Rate with Effective Size Correction", "topics": [ "innocent_id_rate_effective" ] }, { "page": "innocent_id_rate_nominal", "title": "Compute Innocent Suspect ID Rate with Nominal Size Correction", "topics": [ "innocent_id_rate_nominal" ] }, { "page": "innocent_id_rate_uncorrected", "title": "Compute Innocent Suspect ID Rate with No Correction", "topics": [ "innocent_id_rate_uncorrected" ] }, { "page": "install_pywitness", "title": "Install the Audited pyWitness Engine", "topics": [ "install_pywitness" ] }, { "page": "install_r4lineups_python", "title": "Install Python Dependencies for r4lineups", "topics": [ "install_r4lineups_python" ] }, { "page": "line73", "title": "line73", "topics": [ "line73" ] }, { "page": "lineup_bias_boot_dist", "title": "Bootstrap Distribution of Lineup Bias (Target Proportion)", "topics": [ "lineup_bias_boot_dist" ] }, { "page": "lineup_boot_allprop", "title": "Confidence intervals for lineup proportion", "topics": [ "lineup_boot_allprop" ] }, { "page": "lineup_example", "title": "Example Lineup Identification Data", "topics": [ "lineup_example" ] }, { "page": "lineup_pairwise_matrix", "title": "Compute Pairwise Similarity Matrix for All Lineup Members", "topics": [ "lineup_pairwise_matrix" ] }, { "page": "lineup_prop_boot", "title": "Bootstrapped lineup proportion", "topics": [ "lineup_prop_boot" ] }, { "page": "lineup_prop_tab", "title": "Lineup proportion", "topics": [ "lineup_prop_tab" ] }, { "page": "lineup_prop_vec", "title": "Lineup proportion", "topics": [ "lineup_prop_vec" ] }, { "page": "lineup_similarity", "title": "Compute Lineup Similarity Matrix", "topics": [ "lineup_similarity" ] }, { "page": "lineup_table", "title": "Tabulate Lineup Choices, Including Null Foils", "topics": [ "lineup_table" ] }, { "page": "ln_diag_ratio", "title": "Ln of Diagnosticity Ratio", "topics": [ "ln_diag_ratio" ] }, { "page": "make_bayes_curves", "title": "Compute Bayesian Prior-Posterior and Information Gain Curves", "topics": [ "make_bayes_curves" ] }, { "page": "make_bree_curve", "title": "Compute Base-Rate Effect-Equivalency (BREE) Curves", "topics": [ "make_bree_curve" ] }, { "page": "make_cac", "title": "Compute and Plot CAC for Lineup Identification", "topics": [ "make_cac" ] }, { "page": "make_cac_gg", "title": "Plot CAC Curve", "topics": [ "make_cac_gg" ] }, { "page": "make_cacdata", "title": "Compute CAC (Confidence-Accuracy Characteristic) Data", "topics": [ "make_cacdata" ] }, { "page": "make_calibration", "title": "Compute and Plot Calibration for Lineup Identification", "topics": [ "make_calibration" ] }, { "page": "make_calibration_by_condition", "title": "Compute Calibration Statistics by Condition", "topics": [ "make_calibration_by_condition" ] }, { "page": "make_calibration_by_condition_gg", "title": "Plot Calibration Curves by Condition", "topics": [ "make_calibration_by_condition_gg" ] }, { "page": "make_calibration_data", "title": "Compute Calibration Statistics for Eyewitness Identification", "topics": [ "make_calibration_data" ] }, { "page": "make_calibration_gg", "title": "Plot Calibration Curve", "topics": [ "make_calibration_gg" ] }, { "page": "make_dpp", "title": "Compute Deviation from Perfect Performance (DPP)", "topics": [ "make_dpp" ] }, { "page": "make_eig", "title": "Main Function to Compute and Visualize EIG", "topics": [ "make_eig" ] }, { "page": "make_eig_data", "title": "Prepare EIG Data from Lineup Identification", "topics": [ "make_eig_data" ] }, { "page": "make_fullroc", "title": "Compute and Plot Full ROC Curve (Smith & Yang, 2020)", "topics": [ "make_fullroc" ] }, { "page": "make_fullroc_data", "title": "Compute Full ROC Data for Lineup Identification (Smith & Yang, 2020)", "topics": [ "make_fullroc_data" ] }, { "page": "make_multid", "title": "Multi-d' Model of Lineup Performance (Lee & Penrod, 2019)", "topics": [ "make_multid" ] }, { "page": "make_ppv_range", "title": "Main Function to Compute and Visualize PPV Range", "topics": [ "make_ppv_range" ] }, { "page": "make_rac", "title": "Compute and Plot RAC for Lineup Identification", "topics": [ "make_rac" ] }, { "page": "make_rac_gg", "title": "Plot RAC Curve", "topics": [ "make_rac_gg" ] }, { "page": "make_racdata", "title": "Compute RAC (Response Time-Accuracy Characteristic) Data", "topics": [ "make_racdata" ] }, { "page": "make_roc", "title": "Compute and Plot ROC Curve for Lineup Identification", "topics": [ "make_roc" ] }, { "page": "make_roc_gg", "title": "Plot ROC Curve for Lineup Identification", "topics": [ "make_roc_gg" ] }, { "page": "make_rocdata", "title": "Compute ROC Data for Lineup Identification", "topics": [ "make_rocdata" ] }, { "page": "make_utility_curves", "title": "Compute Expected Utility Curves for Lineup Identification", "topics": [ "make_utility_curves" ] }, { "page": "make_utility_difference", "title": "Compute Utility Difference Curves Comparing Two Procedures", "topics": [ "make_utility_difference" ] }, { "page": "makevec_prop", "title": "Helper functions", "topics": [ "makevec_prop" ] }, { "page": "max_filler_moments", "title": "Max Filler Distribution Moments", "topics": [ "max_filler_moments" ] }, { "page": "mickwick", "title": "Confidence & Accuracy data (Mickes & Wixted)", "topics": [ "mickwick" ] }, { "page": "mockdata", "title": "mockdata", "topics": [ "mockdata" ] }, { "page": "nortje2012", "title": "nortje2012", "topics": [ "nortje2012" ] }, { "page": "plot_2ht_fit", "title": "Plot Observed vs. Expected Counts", "topics": [ "plot_2ht_fit" ] }, { "page": "plot_2ht_parameters", "title": "Plot Parameter Estimates from 2-HT Model", "topics": [ "plot_2ht_parameters" ] }, { "page": "plot_anri_comparison", "title": "Plot ANRI Comparison Between Groups", "topics": [ "plot_anri_comparison" ] }, { "page": "plot_anri_difference_distribution", "title": "Plot Bootstrap Distribution of ANRI Difference", "topics": [ "plot_anri_difference_distribution" ] }, { "page": "plot_bayes_information_gain", "title": "Plot Information Gain Curves", "topics": [ "plot_bayes_information_gain" ] }, { "page": "plot_bayes_prior_posterior", "title": "Plot Prior-Posterior Curves", "topics": [ "plot_bayes_prior_posterior" ] }, { "page": "plot_bree", "title": "Plot BREE Curve", "topics": [ "plot_bree" ] }, { "page": "plot_dpp", "title": "Plot DPP Across Operating Points", "topics": [ "plot_dpp" ] }, { "page": "plot_dpp_comparison", "title": "Plot DPP Comparison Between Two Procedures", "topics": [ "plot_dpp_comparison" ] }, { "page": "plot_effective_size_conf", "title": "Plot Effective Size by Confidence Level", "topics": [ "plot_effective_size_conf" ] }, { "page": "plot_eig", "title": "Plot Information Gain by Response Category", "topics": [ "plot_eig" ] }, { "page": "plot_eig_posteriors", "title": "Plot Posterior Probabilities for Response Categories", "topics": [ "plot_eig_posteriors" ] }, { "page": "plot_embedding_space", "title": "Plot Embedding Space", "topics": [ "plot_embedding_space" ] }, { "page": "plot_error_rate_conf", "title": "Plot Error Rate by Confidence Level", "topics": [ "plot_error_rate_conf" ] }, { "page": "plot_esize_distribution", "title": "Plot Bootstrap Distribution of Effective Size", "topics": [ "plot_esize_distribution" ] }, { "page": "plot_fullroc", "title": "Plot Full ROC Curve", "topics": [ "plot_fullroc" ] }, { "page": "plot_lineup_bias_distribution", "title": "Plot Bootstrap Distribution of Lineup Bias", "topics": [ "plot_lineup_bias_distribution" ] }, { "page": "plot_lineup_similarity", "title": "Plot Lineup Similarity Results", "topics": [ "plot_lineup_similarity" ] }, { "page": "plot_msdt_joint", "title": "Plot mSDT Joint Distributions", "topics": [ "plot_msdt_joint" ] }, { "page": "plot_ppv_range", "title": "Plot PPV Range Across Confidence Levels", "topics": [ "plot_ppv_range" ] }, { "page": "plot_roc_cac", "title": "Plot Methods for ROC and CAC Objects", "topics": [ "plot.lineup_cac", "plot.lineup_cac_data", "plot.lineup_fullroc", "plot.lineup_fullroc_data", "plot.lineup_roc", "plot.lineup_roc_data", "plot_roc_cac" ] }, { "page": "plot_similarity_distribution", "title": "Plot Similarity Distribution", "topics": [ "plot_similarity_distribution" ] }, { "page": "plot_utility_curves", "title": "Plot Expected Utility Curves", "topics": [ "plot_utility_curves" ] }, { "page": "plot_utility_difference", "title": "Plot Utility Difference Curve", "topics": [ "plot_utility_difference" ] }, { "page": "plot.esize_T_bayes", "title": "Plot method for esize_T_bayes objects", "topics": [ "plot.esize_T_bayes" ] }, { "page": "plot.esize_T_bayes_compare", "title": "Plot method for esize_T_bayes_compare objects", "topics": [ "plot.esize_T_bayes_compare" ] }, { "page": "plot.lineup_model_comparison", "title": "Plot a Wixted Lineup-Memory Model Comparison", "topics": [ "plot.lineup_model_comparison" ] }, { "page": "plot.model_comparison", "title": "Plot Side-by-Side Model Comparisons", "topics": [ "plot.model_comparison" ] }, { "page": "plot.multid", "title": "Plot a Multi-d' Analysis", "topics": [ "plot.multid" ] }, { "page": "plot.pauc_comparison", "title": "Plot pAUC Comparison", "topics": [ "plot.pauc_comparison" ] }, { "page": "plot.power_analysis", "title": "Plot Power Analysis Results", "topics": [ "plot.power_analysis" ] }, { "page": "plot.sdt_roc_fit", "title": "Plot Method for SDT ROC Fit", "topics": [ "plot.sdt_roc_fit" ] }, { "page": "plot.winter_2ht", "title": "Plot Method for winter_2ht Objects", "topics": [ "plot.winter_2ht" ] }, { "page": "plot.winter_2ht_boot", "title": "Plot Bootstrap Distributions", "topics": [ "plot.winter_2ht_boot" ] }, { "page": "ppv_by_confidence", "title": "Compute PPV by Confidence with Lineup Size Correction", "topics": [ "ppv_by_confidence" ] }, { "page": "ppv_range_by_confidence", "title": "Compute PPV Range by Confidence (All Three Corrections)", "topics": [ "ppv_range_by_confidence" ] }, { "page": "print.esize_T_bayes", "title": "Print method for esize_T_bayes objects", "topics": [ "print.esize_T_bayes" ] }, { "page": "print.esize_T_bayes_compare", "title": "Print method for esize_T_bayes_compare objects", "topics": [ "print.esize_T_bayes_compare" ] }, { "page": "print.lineup_anri", "title": "Print Method for ANRI Objects", "topics": [ "print.lineup_anri" ] }, { "page": "print.lineup_anri_comparison", "title": "Print Method for ANRI Comparison Objects", "topics": [ "print.lineup_anri_comparison" ] }, { "page": "print.lineup_bayes_curves", "title": "Print Method for Bayes Curves Objects", "topics": [ "print.lineup_bayes_curves" ] }, { "page": "print.lineup_cac", "title": "Print Method for lineup_cac Objects", "topics": [ "print.lineup_cac" ] }, { "page": "print.lineup_calibration", "title": "Print Method for lineup_calibration Objects", "topics": [ "print.lineup_calibration" ] }, { "page": "print.lineup_data", "title": "Print Method for Lineup Data", "topics": [ "print.lineup_data" ] }, { "page": "print.lineup_dpp", "title": "Print Method for lineup_dpp Objects", "topics": [ "print.lineup_dpp" ] }, { "page": "print.lineup_dpp_comparison", "title": "Print Method for lineup_dpp_comparison Objects", "topics": [ "print.lineup_dpp_comparison" ] }, { "page": "print.lineup_eig", "title": "Print Method for lineup_eig Objects", "topics": [ "print.lineup_eig" ] }, { "page": "print.lineup_fullroc", "title": "Print Method for lineup_fullroc Objects", "topics": [ "print.lineup_fullroc" ] }, { "page": "print.lineup_ppv", "title": "Print Method for lineup_ppv Objects", "topics": [ "print.lineup_ppv" ] }, { "page": "print.lineup_ppv_range", "title": "Print Method for lineup_ppv_range Objects", "topics": [ "print.lineup_ppv_range" ] }, { "page": "print.lineup_rac", "title": "Print Method for lineup_rac Objects", "topics": [ "print.lineup_rac" ] }, { "page": "print.lineup_roc", "title": "Print Method for lineup_roc Objects", "topics": [ "print.lineup_roc" ] }, { "page": "print.lineup_similarity", "title": "Print Method for lineup_similarity", "topics": [ "print.lineup_similarity" ] }, { "page": "print.lineup_table", "title": "Print Method for lineup_table Objects", "topics": [ "print.lineup_table" ] }, { "page": "print.lineup_utility_comparison", "title": "Print Method for lineup_utility_comparison Objects", "topics": [ "print.lineup_utility_comparison" ] }, { "page": "print.model_comparison", "title": "Print Method for model_comparison Objects", "topics": [ "print.model_comparison" ] }, { "page": "print.pauc_comparison", "title": "Print Method for pauc_comparison Objects", "topics": [ "print.pauc_comparison" ] }, { "page": "print.sdt_roc_fit", "title": "Print Method for SDT ROC Fit", "topics": [ "print.sdt_roc_fit" ] }, { "page": "print.simulated_lineup_data", "title": "Print Method for Simulated Lineup Data", "topics": [ "print.simulated_lineup_data" ] }, { "page": "print.winter_2ht", "title": "Print Method for winter_2ht Objects", "topics": [ "print.winter_2ht" ] }, { "page": "print.winter_2ht_boot", "title": "Print Method for winter_2ht_boot Objects", "topics": [ "print.winter_2ht_boot" ] }, { "page": "rep_index", "title": "Rep index", "topics": [ "rep_index" ] }, { "page": "rot_vector", "title": "Rotate vector", "topics": [ "rot_vector" ] }, { "page": "run_r4lineups_app", "title": "Run the r4lineups Shiny app", "topics": [ "run_r4lineups_app" ] }, { "page": "sdt_compare", "title": "Bayesian Comparison of SDT Sensitivity (d') Between Two Conditions", "topics": [ "sdt_compare" ] }, { "page": "sdt_summary_from_counts", "title": "SDT Summary Statistics from Counts", "topics": [ "sdt_summary_from_counts" ] }, { "page": "sdt_summary_variance", "title": "Variance Estimates for SDT Summary Metrics", "topics": [ "sdt_summary_variance" ] }, { "page": "simulate_lineup_data", "title": "Simulate Lineup Identification Data", "topics": [ "simulate_lineup_data" ] }, { "page": "simulate_power_analysis", "title": "Simulate Power Analysis for Lineup Studies", "topics": [ "simulate_power_analysis" ] }, { "page": "standardize_lineup_data", "title": "Standardize Lineup Identification Data", "topics": [ "standardize_lineup_data" ] }, { "page": "summary.lineup_eig", "title": "Summary Method for lineup_eig Objects", "topics": [ "summary.lineup_eig" ] }, { "page": "summary.lineup_similarity", "title": "Summary Method for lineup_similarity", "topics": [ "summary.lineup_similarity" ] }, { "page": "summary.model_comparison", "title": "Summary Method for model_comparison Objects", "topics": [ "summary.model_comparison" ] }, { "page": "summary.pauc_comparison", "title": "Summary Method for pauc_comparison Objects", "topics": [ "summary.pauc_comparison" ] }, { "page": "summary.sdt_roc_fit", "title": "Summary Method for SDT ROC Fit", "topics": [ "summary.sdt_roc_fit" ] }, { "page": "summary.winter_2ht", "title": "Summary Method for winter_2ht Objects", "topics": [ "summary.winter_2ht" ] }, { "page": "summary.winter_2ht_boot", "title": "Summary Method for winter_2ht_boot Objects", "topics": [ "summary.winter_2ht_boot" ] }, { "page": "validate_lineup_data", "title": "Validate Lineup Identification Data", "topics": [ "validate_lineup_data" ] }, { "page": "var_diag_ratio", "title": "Asymptotic Variance of Log Diagnosticity", "topics": [ "var_diag_ratio" ] }, { "page": "var_lnd", "title": "Variance of ln of diagnosticity ratio", "topics": [ "var_lnd" ] } ], "_readme": "https://github.com/CGTZA2/r4lineups/raw/main/README.md", "_rundeps": [ "boot", "cli", "cpp11", "curl", "dplyr", "farver", "generics", "ggplot2", "ggrepel", 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Tredoux, Tamsyn M. Naylor", "engine": "knitr::rmarkdown", "headings": [ "Purpose", "If you have only used frequentist methods", "Data Shapes", "Effective Size", "Functional Size", "Diagnosticity Ratio", "Calibration", "SDT d' Comparison", "Workflow Position", "Technical Details", "What is being sampled", "The prior", "Monte Carlo error", "Assumptions", "References", "Further Reading" ], "created": "2026-06-21 20:53:56", "modified": "2026-10-31 17:35:08", "commits": 3 }, { "source": "calibration_decision_analysis.Rmd", "filename": "calibration_decision_analysis.html", "title": "Calibration and Decision Analysis", "author": "Colin G. Tredoux, Tamsyn M. Naylor", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Data Format", "Why there is a lineup_member column", "1. Calibration Analysis", "Basic Calibration", "Interpreting the Calibration Plot", "Calibration by Condition", "2. 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Naylor", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "The Three Models", "2-HT Model: Latent Cognitive Processes", "EIG: Information-Theoretic Measure", "Full ROC: Investigator Discriminability", "Basic Model Comparison", "Loading Data", "Comparing All Models", "Detailed Summary", "Visualizing Model Comparisons", "Individual Model Access", "2-HT Model Results", "EIG Results", "Full ROC Results", "Method-Choice Guidance", "When to Use Each Model", "Comparing Model Fits", "Advanced Usage", "Fitting Specific Models Only", "Using Confidence Bins", "Custom Prior for EIG", "Simulated Data Example", "Comparing Models with Known Parameters", "Sensitivity to the Generating d′", "Practical Examples", "Example 1: Evaluating a Lineup Procedure", "Example 2: Identifying Sources of Bias", "Example 3: Comparing Detection vs. Guessing", "Formatted Comparison Tables", "Console Format", "Markdown Format", "Interpretation Guidelines", "Common Pitfalls", "1. Insufficient Sample Size", "2. 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Naylor", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Signal Detection Theory, briefly", "Decision rules", "The criterion, in plainer terms", "Basic Data Simulation", "Power Analysis", "What the function does", "A worked power analysis", "Plotting, and adjusting the plot", "Method Check: Sensitivity to d′", "Comparing Experimental Designs", "Integration with r4lineups Analyses", "ROC Analysis", "CAC Analysis", "RAC Analysis", "Full ROC Analysis", "Choosing Parameter Values", "Summary", "References", "Further Reading" ], "created": "2026-01-30 19:01:47", "modified": "2026-11-02 09:23:41", "commits": 7 }, { "source": "face_similarity.Rmd", "filename": "face_similarity.html", "title": "Face Similarity Analysis", "author": "Colin G. Tredoux, Tamsyn M. Naylor", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Why measure face similarity?", "An important caution before going further", "What This Vignette Covers", "Setup", "Check Current Setup", "Install Python Dependencies", "If the Python setup defeats you", "Verify Installation", "Basic Face Comparison", "Compare Two Faces", "Understanding the Output", "Lineup Similarity Analysis", "Analyze Complete Lineup", "Examining Results", "Describing the similarity profile", "Models and Metrics", "Available Models", "Distance Metrics", "Comparing Models", "Visualization", "Display Lineup with Similarity Scores", "Similarity Heatmap", "Similarity Distribution", "Advanced Features", "Extract Face Embeddings", "Dimensionality Reduction Visualization", "Batch Processing Multiple Lineups", "Integration with Traditional Measures", "Compare with Effective Size", "Similarity and mock-witness choices: a hypothesis to test", "Validation Studies", "Practical Guidelines", "Interpreting Similarity Scores", "Best Practices", "Troubleshooting", "Example Workflow", "References", "Technical Details", "What is computed", "Assumptions and limitations", "Reproducibility", "Session Info" ], "created": "2026-06-21 20:53:56", "modified": "2026-10-31 17:35:08", "commits": 3 }, { "source": "lineup_memory_models.Rmd", "filename": "lineup_memory_models.html", "title": "Fitting Wixted Lineup-Memory Models", "author": "Colin G. Tredoux, Tamsyn M. Naylor", "engine": "knitr::rmarkdown", "headings": [ "What these models are", "Why it is important to know which one is right", "What this interface does and does not cover", "Installing the engine", "Preparing data", "Fitting", "Can the fit tell the generating rules apart?", "Interpreting the output", "Technical Details", "How the models are estimated", "Why AIC and BIC are NA", "What to check before interpreting", "Assumptions", "References", "Further Reading" ], "created": "2026-08-10 15:22:38", "modified": "2026-10-31 17:35:08", "commits": 3 }, { "source": "fullroc_analysis.Rmd", "filename": "fullroc_analysis.html", "title": "Full ROC Analysis for Eyewitness Lineups", "author": "Colin G. Tredoux, Tamsyn M. Naylor", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Every response, not just suspect identifications", "Why the partial ROC curve does not span the full range", "The key idea: two signal detection tasks", "Basic usage", "Data structure", "Compute a full ROC", "Ordering methods, and a warning about the default", "A-priori ordering", "Diagnosticity ordering", "How large is the bias?", "Binning confidence", "Understanding the output", "The diagnosticity table", "The ROC data", "Interpreting the AUC", "Comparing procedures", "Technical Details", "How the curve is built", "Edge cases", "Assumptions", "No standard errors", "Summary", "References", "Further Reading" ], "created": "2026-01-30 08:50:31", "modified": "2026-10-31 17:35:08", "commits": 5 }, { "source": "information_error_rate_analysis.Rmd", "filename": "information_error_rate_analysis.html", "title": "Information and Error-Rate Metrics", "author": "Colin G. Tredoux, Tamsyn M. 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