allfoil_cihigh          Confidence Intervals for Proportion
allfoilbias             Bias for each lineup member
allprop                 Lineup proportion for all lineup members
available_detectors     List Available Face Detectors
available_models        List Available Face Recognition Models
batch_embeddings        Extract Embeddings for Multiple Images
boot_winter_2ht         Bootstrap Confidence Intervals for 2-HT Model
                        Parameters
bootstrap_anri          Bootstrap Confidence Intervals for ANRI
calibration_bayes       Bayesian Calibration Analysis (Beta-Binomial
                        Model)
check_python_deps       Check Python Dependencies
check_pywitness_deps    Check Availability of the pyWitness Engine
chi_diag                Chi-squared estimate of homogeneity of
                        diagnosticity ratio
classify_lineup_choices
                        Classify Raw Lineup Choices into Identification
                        Outcomes
compare_anri            Compare ANRI Between Groups with Bootstrap
compare_dpp             Compare DPP Between Two Procedures
compare_eff_sizes.boot
                        Comparing Effective Size: Base function for
                        bootstrapping
compare_max_sdt         Compare Two Nested MAX SDT Models
compare_models          Compare Multiple Models for Lineup
                        Identification Data
compare_multid          Compare Multi-d' Indices Between Two Conditions
compare_pauc            Compare pAUC Between Two ROC Curves
compare_sdt_summary     Compare SDT Summary Metrics Between Two
                        Conditions
compare_utility         Compare Utilities for Two Procedures
compute_anri            Compute ANRI (Adjusted Normalized Resolution
                        Index)
compute_dpp             Compute and Plot DPP
compute_eig             Compute Expected Information Gain (EIG)
cosine_to_similarity    Convert Cosine Distance to Similarity Score
create_example_lineup_data
                        Create Example Lineup Data
d_bar                   Mean diagnosticity ratio for k lineup pairs
d_weights               Diagnosticity ratio weights
datacheck1              Helper function
datacheck2              Helper function
datacheck3              Helper function
detect_faces            Detect Faces in an Image
diag_param              Parameters for diagnosticity ratio
diag_ratio_T            Diagnosticity Ratio (Tredoux, 1998)
diag_ratio_T_bayes      Bayesian Diagnosticity Ratio (Tredoux,
                        Beta-Binomial Model)
diag_ratio_T_bayes_compare
                        Bayesian Comparison of Two Diagnosticity Ratios
diag_ratio_W            Diagnosticity Ratio (Wells & Lindsay, 1980;
                        Wells & Turtle, 1986)
display_lineup          Display Lineup as Image Grid
dmax_filler             mSDT: Max Filler Distribution (IID Normal
                        Fillers)
eff_size_per_foils      Effective Size per Foils
effsize_compare         Master Function: Comparing Effective Size
embedding_distance      Compute Distance Between Two Embeddings
esize_T                 Effective Size (Tredoux, 1998)
esize_T_bayes           Bayesian Posterior Distribution of Effective
                        Size (Tredoux, 1998)
esize_T_bayes_compare   Bayesian Comparison of Effective Size (Tredoux,
                        1998) Across Two Lineups
esize_T_boot            Bootstrapped Effective Size (Tredoux, 1998)
esize_T_ci_n            Effective Size with Confidence Intervals from
                        Normal Theory (Tredoux, 1998)
esize_boot_dist         Bootstrap Distribution of Effective Size
esize_m                 Effective Size
esize_m_boot            Bootstrapped Effective Size
estimate_msdt_params    mSDT Parameter Estimation from Rejection Rates
extract_sdt_metrics     Extract SDT Metrics from a GLM/GLMM
face_similarity         Compute Face Similarity Between Two Images
fit_lineup_models       Fit Competing Wixted Lineup-Memory Models
fit_max_sdt             Restricted Independent-Observations/MAX Model
                        for Lineup Counts
fit_sdt_glm             Fit an SDT GLM for Old/New Recognition
fit_sdt_glmm            Fit an SDT GLMM for Old/New Recognition
fit_sdt_roc             Fit Signal Detection Theory Model to ROC Data
fit_winter_2ht          Winter et al. (2022) Two-High Threshold (2-HT)
                        MPT Model for Eyewitness Identification
format_comparison_table
                        Create Comprehensive Model Comparison Table
func_size               Functional Size
func_size.boot          Bootstrapped Functional Size
func_size_bayes         Bayesian Functional Size (Beta-Binomial Model)
func_size_report        Functional Size with Bootstrapped Confidence
                        Intervals
gen_boot_propci         Percentile of Bootstrapped Lineup Proportion
gen_boot_propmean_se    Descriptive statistics for bootstrapped lineup
                        proportion
gen_boot_samples        Bootstrap resampling
gen_boot_samples_list   Bootstrapped resampling
gen_esize_m             Effective Size (across a dataframe)
gen_esize_m_ci          Bootstrap Quantile for Effective Size
gen_lineup_prop         Lineup proportion over dataframe
gen_linevec             Lineup vector
get_embedding           Extract Face Embedding from an Image
get_face_landmarks      Get 478-Point Face Mesh Landmarks
get_key_landmarks       Get Key Facial Landmarks (5-Point)
has_valid_face          Check if Image Contains a Valid Face
homog_diag              Master function: Homogeneity of diagnosticity
                        ratio
homog_diag_boot         Homogeneity of diagnosticity ratio with
                        bootstrapped CIs
i_esize_T               I Component of Effective Size (Tredoux, 1998)
innocent_id_rate_effective
                        Compute Innocent Suspect ID Rate with Effective
                        Size Correction
innocent_id_rate_nominal
                        Compute Innocent Suspect ID Rate with Nominal
                        Size Correction
innocent_id_rate_uncorrected
                        Compute Innocent Suspect ID Rate with No
                        Correction
install_pywitness       Install the Audited pyWitness Engine
install_r4lineups_python
                        Install Python Dependencies for r4lineups
line73                  line73
lineup_bias_boot_dist   Bootstrap Distribution of Lineup Bias (Target
                        Proportion)
lineup_boot_allprop     Confidence intervals for lineup proportion
lineup_example          Example Lineup Identification Data
lineup_pairwise_matrix
                        Compute Pairwise Similarity Matrix for All
                        Lineup Members
lineup_prop_boot        Bootstrapped lineup proportion
lineup_prop_tab         Lineup proportion
lineup_prop_vec         Lineup proportion
lineup_similarity       Compute Lineup Similarity Matrix
lineup_table            Tabulate Lineup Choices, Including Null Foils
ln_diag_ratio           Ln of Diagnosticity Ratio
make_bayes_curves       Compute Bayesian Prior-Posterior and
                        Information Gain Curves
make_bree_curve         Compute Base-Rate Effect-Equivalency (BREE)
                        Curves
make_cac                Compute and Plot CAC for Lineup Identification
make_cac_gg             Plot CAC Curve
make_cacdata            Compute CAC (Confidence-Accuracy
                        Characteristic) Data
make_calibration        Compute and Plot Calibration for Lineup
                        Identification
make_calibration_by_condition
                        Compute Calibration Statistics by Condition
make_calibration_by_condition_gg
                        Plot Calibration Curves by Condition
make_calibration_data   Compute Calibration Statistics for Eyewitness
                        Identification
make_calibration_gg     Plot Calibration Curve
make_dpp                Compute Deviation from Perfect Performance
                        (DPP)
make_eig                Main Function to Compute and Visualize EIG
make_eig_data           Prepare EIG Data from Lineup Identification
make_fullroc            Compute and Plot Full ROC Curve (Smith & Yang,
                        2020)
make_fullroc_data       Compute Full ROC Data for Lineup Identification
                        (Smith & Yang, 2020)
make_multid             Multi-d' Model of Lineup Performance (Lee &
                        Penrod, 2019)
make_ppv_range          Main Function to Compute and Visualize PPV
                        Range
make_rac                Compute and Plot RAC for Lineup Identification
make_rac_gg             Plot RAC Curve
make_racdata            Compute RAC (Response Time-Accuracy
                        Characteristic) Data
make_roc                Compute and Plot ROC Curve for Lineup
                        Identification
make_roc_gg             Plot ROC Curve for Lineup Identification
make_rocdata            Compute ROC Data for Lineup Identification
make_utility_curves     Compute Expected Utility Curves for Lineup
                        Identification
make_utility_difference
                        Compute Utility Difference Curves Comparing Two
                        Procedures
makevec_prop            Helper functions
max_filler_moments      Max Filler Distribution Moments
mickwick                Confidence & Accuracy data (Mickes & Wixted)
mockdata                mockdata
nortje2012              nortje2012
plot.esize_T_bayes      Plot method for esize_T_bayes objects
plot.esize_T_bayes_compare
                        Plot method for esize_T_bayes_compare objects
plot.lineup_model_comparison
                        Plot a Wixted Lineup-Memory Model Comparison
plot.model_comparison   Plot Side-by-Side Model Comparisons
plot.multid             Plot a Multi-d' Analysis
plot.pauc_comparison    Plot pAUC Comparison
plot.power_analysis     Plot Power Analysis Results
plot.sdt_roc_fit        Plot Method for SDT ROC Fit
plot.winter_2ht         Plot Method for winter_2ht Objects
plot.winter_2ht_boot    Plot Bootstrap Distributions
plot_2ht_fit            Plot Observed vs. Expected Counts
plot_2ht_parameters     Plot Parameter Estimates from 2-HT Model
plot_anri_comparison    Plot ANRI Comparison Between Groups
plot_anri_difference_distribution
                        Plot Bootstrap Distribution of ANRI Difference
plot_bayes_information_gain
                        Plot Information Gain Curves
plot_bayes_prior_posterior
                        Plot Prior-Posterior Curves
plot_bree               Plot BREE Curve
plot_dpp                Plot DPP Across Operating Points
plot_dpp_comparison     Plot DPP Comparison Between Two Procedures
plot_effective_size_conf
                        Plot Effective Size by Confidence Level
plot_eig                Plot Information Gain by Response Category
plot_eig_posteriors     Plot Posterior Probabilities for Response
                        Categories
plot_embedding_space    Plot Embedding Space
plot_error_rate_conf    Plot Error Rate by Confidence Level
plot_esize_distribution
                        Plot Bootstrap Distribution of Effective Size
plot_fullroc            Plot Full ROC Curve
plot_lineup_bias_distribution
                        Plot Bootstrap Distribution of Lineup Bias
plot_lineup_similarity
                        Plot Lineup Similarity Results
plot_msdt_joint         Plot mSDT Joint Distributions
plot_ppv_range          Plot PPV Range Across Confidence Levels
plot_roc_cac            Plot Methods for ROC and CAC Objects
plot_similarity_distribution
                        Plot Similarity Distribution
plot_utility_curves     Plot Expected Utility Curves
plot_utility_difference
                        Plot Utility Difference Curve
ppv_by_confidence       Compute PPV by Confidence with Lineup Size
                        Correction
ppv_range_by_confidence
                        Compute PPV Range by Confidence (All Three
                        Corrections)
print.esize_T_bayes     Print method for esize_T_bayes objects
print.esize_T_bayes_compare
                        Print method for esize_T_bayes_compare objects
print.lineup_anri       Print Method for ANRI Objects
print.lineup_anri_comparison
                        Print Method for ANRI Comparison Objects
print.lineup_bayes_curves
                        Print Method for Bayes Curves Objects
print.lineup_cac        Print Method for lineup_cac Objects
print.lineup_calibration
                        Print Method for lineup_calibration Objects
print.lineup_data       Print Method for Lineup Data
print.lineup_dpp        Print Method for lineup_dpp Objects
print.lineup_dpp_comparison
                        Print Method for lineup_dpp_comparison Objects
print.lineup_eig        Print Method for lineup_eig Objects
print.lineup_fullroc    Print Method for lineup_fullroc Objects
print.lineup_ppv        Print Method for lineup_ppv Objects
print.lineup_ppv_range
                        Print Method for lineup_ppv_range Objects
print.lineup_rac        Print Method for lineup_rac Objects
print.lineup_roc        Print Method for lineup_roc Objects
print.lineup_similarity
                        Print Method for lineup_similarity
print.lineup_table      Print Method for lineup_table Objects
print.lineup_utility_comparison
                        Print Method for lineup_utility_comparison
                        Objects
print.model_comparison
                        Print Method for model_comparison Objects
print.pauc_comparison   Print Method for pauc_comparison Objects
print.sdt_roc_fit       Print Method for SDT ROC Fit
print.simulated_lineup_data
                        Print Method for Simulated Lineup Data
print.winter_2ht        Print Method for winter_2ht Objects
print.winter_2ht_boot   Print Method for winter_2ht_boot Objects
rep_index               Rep index
rot_vector              Rotate vector
run_r4lineups_app       Run the r4lineups Shiny app
sdt_compare             Bayesian Comparison of SDT Sensitivity (d')
                        Between Two Conditions
sdt_summary_from_counts
                        SDT Summary Statistics from Counts
sdt_summary_variance    Variance Estimates for SDT Summary Metrics
simulate_lineup_data    Simulate Lineup Identification Data
simulate_power_analysis
                        Simulate Power Analysis for Lineup Studies
standardize_lineup_data
                        Standardize Lineup Identification Data
summary.lineup_eig      Summary Method for lineup_eig Objects
summary.lineup_similarity
                        Summary Method for lineup_similarity
summary.model_comparison
                        Summary Method for model_comparison Objects
summary.pauc_comparison
                        Summary Method for pauc_comparison Objects
summary.sdt_roc_fit     Summary Method for SDT ROC Fit
summary.winter_2ht      Summary Method for winter_2ht Objects
summary.winter_2ht_boot
                        Summary Method for winter_2ht_boot Objects
validate_lineup_data    Validate Lineup Identification Data
var_diag_ratio          Asymptotic Variance of Log Diagnosticity
var_lnd                 Variance of ln of diagnosticity ratio
