Statistical Inference on Lineup Fairness

Documentation for package ‘r4lineups’ version 2.3.0

Help Pages

-- A --

allfoilbias	Bias for each lineup member
allfoil_cihigh	Confidence Intervals for Proportion
allprop	Lineup proportion for all lineup members
available_detectors	List Available Face Detectors
available_models	List Available Face Recognition Models

-- B --

batch_embeddings	Extract Embeddings for Multiple Images
bootstrap_anri	Bootstrap Confidence Intervals for ANRI
boot_winter_2ht	Bootstrap Confidence Intervals for 2-HT Model Parameters

-- C --

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 --

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)
d_bar	Mean diagnosticity ratio for k lineup pairs
d_weights	Diagnosticity ratio weights

-- E --

effsize_compare	Master Function: Comparing Effective Size
eff_size_per_foils	Effective Size per Foils
embedding_distance	Compute Distance Between Two Embeddings
esize_boot_dist	Bootstrap Distribution of Effective Size
esize_m	Effective Size
esize_m_boot	Bootstrapped Effective Size
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)
estimate_msdt_params	mSDT Parameter Estimation from Rejection Rates
extract_sdt_metrics	Extract SDT Metrics from a GLM/GLMM

-- F --

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

-- G --

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)

-- H --

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 --

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
i_esize_T	I Component of Effective Size (Tredoux, 1998)

-- L --

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

-- M --

makevec_prop	Helper functions
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_cacdata	Compute CAC (Confidence-Accuracy Characteristic) Data
make_cac_gg	Plot CAC Curve
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_racdata	Compute RAC (Response Time-Accuracy Characteristic) Data
make_rac_gg	Plot RAC Curve
make_roc	Compute and Plot ROC Curve for Lineup Identification
make_rocdata	Compute ROC Data for Lineup Identification
make_roc_gg	Plot ROC Curve for Lineup Identification
make_utility_curves	Compute Expected Utility Curves for Lineup Identification
make_utility_difference	Compute Utility Difference Curves Comparing Two Procedures
max_filler_moments	Max Filler Distribution Moments
mickwick	Confidence & Accuracy data (Mickes & Wixted)
mockdata	mockdata
msdt_dprime_from_rej	mSDT Parameter Estimation from Rejection Rates
msdt_gamma_from_rej	mSDT Parameter Estimation from Rejection Rates

-- N --

nortje2012

-- P --

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_cac	Plot Methods for ROC and CAC Objects
plot.lineup_cac_data	Plot Methods for ROC and CAC Objects
plot.lineup_fullroc	Plot Methods for ROC and CAC Objects
plot.lineup_fullroc_data	Plot Methods for ROC and CAC Objects
plot.lineup_model_comparison	Plot a Wixted Lineup-Memory Model Comparison
plot.lineup_roc	Plot Methods for ROC and CAC Objects
plot.lineup_roc_data	Plot Methods for ROC and CAC Objects
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
pmax_filler	mSDT: Max Filler Distribution (IID Normal Fillers)
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

-- Q --

qmax_filler

mSDT: Max Filler Distribution (IID Normal Fillers)

-- R --

rep_index	Rep index
rmax_filler	mSDT: Max Filler Distribution (IID Normal Fillers)
rot_vector	Rotate vector
run_r4lineups_app	Run the r4lineups Shiny app

-- S --

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

-- V --

validate_lineup_data	Validate Lineup Identification Data
var_diag_ratio	Asymptotic Variance of Log Diagnosticity
var_lnd	Variance of ln of diagnosticity ratio