Package 'r4lineups'

Description

Function to compute ci high for each foil in a lineup

Usage

allfoil_cihigh(linetabprops, sumlineup)

Arguments

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Examples

# Upper CI bounds for the choice proportions of three lineup members,
# based on 20 mock witnesses
allfoil_cihigh(c(0.5, 0.3, 0.2), 20)

Description

Function to compute bias for each lineup member (assuming foil is suspect, from Malpass, 1981)

Usage

allfoilbias(lineup_tab, target_pos, k)

Arguments

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Examples

lineup_vec <- round(runif(100, 1, 6))
lineup_tab <- table(lineup_vec)
x <- allfoilbias(lineup_tab, 5, 6)

Description

Computes lineup proportion for each member in a lineup

Usage

allprop(lineup_vec, k)

Arguments

Value

Returns a vector containing lineup proportion for each lineup member

References

Wells, G. L., Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Call:
x <- allprop(lineup_vec, k = 6)

Description

Returns a character vector of available face detection backends.

Usage

available_detectors()

Details

Available detectors (in rough order of accuracy/speed tradeoff):

• retinaface - Most accurate, recommended for research

• mtcnn - Good accuracy, moderate speed

• mediapipe - Fast, good for real-time

• opencv - Fastest, less accurate

• ssd - Fast CNN-based

• dlib - Classic HOG-based

• yolov8 - YOLO-based detection

• centerface - Anchor-free detection

• skip - Skip detection (use full image)

Value

Character vector of detector names.

Examples

available_detectors()

Description

Returns a character vector of available face recognition models that can be used for embedding extraction.

Usage

available_models()

Details

Available models with their embedding dimensions:

• VGG-Face (4096-d) - Legacy model

• Facenet (128-d) - Google's FaceNet

• Facenet512 (512-d) - FaceNet with larger embeddings

• OpenFace (128-d) - Open source

• DeepFace (4096-d) - Facebook's model

• DeepID (160-d) - Compact model

• ArcFace (512-d) - State-of-the-art, recommended

• Dlib (128-d) - Lightweight

• SFace (128-d) - Lightweight

• GhostFaceNet (512-d) - Efficient

Value

Character vector of model names.

Examples

available_models()

Description

Efficiently extracts face embeddings from multiple images.

Usage

batch_embeddings(
  img_paths,
  model = "ArcFace",
  detector = "retinaface",
  align = TRUE,
  enforce_detection = TRUE,
  progress = TRUE
)

Arguments

Value

A tibble with columns:

• img_path - Path to the image

• embedding - List column containing embedding vectors

• facial_area - List column with face bounding box

• face_confidence - Detection confidence

• success - Logical indicating if extraction succeeded

• error - Error message if extraction failed

Examples

## Not run: 
# Get embeddings for all images in a folder
img_files <- list.files("faces/", pattern = "\\.jpg$", full.names = TRUE)
embeddings <- batch_embeddings(img_files)

# Access individual embeddings
embeddings$embedding[[1]]

## End(Not run)

Description

Computes bootstrap confidence intervals for the parameters of the 2-HT model by resampling the observed data with replacement.

Usage

boot_winter_2ht(
  x,
  nboot = 1000,
  conf_level = 0.95,
  lineup_size = 6,
  method = c("percentile", "bca"),
  parallel = FALSE,
  ncpus = 2,
  seed = NULL,
  ...
)

Arguments

Details

The bootstrap procedure resamples within TP and TA strata with replacement to keep both experimental totals fixed. For each bootstrap sample, the 2-HT model is re-fitted, and the distribution of parameter estimates across bootstrap samples is used to construct confidence intervals.

Two types of confidence intervals are available:

• percentile: Uses quantiles of the bootstrap distribution

• bca: Bias-corrected and accelerated intervals (Efron, 1987). The bias correction counts bootstrap ties with the estimate as half, and the acceleration comes from the jackknife over individual observations, centered and scaled within TP/TA strata, using at most six distinct leave-one-out response-count fits.

Value

An object of class "winter_2ht_boot" containing:

References

Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82(397), 171-185.

Examples

counts <- c(
  n_tp_suspect = 147, n_tp_filler = 94, n_tp_reject = 141,
  n_ta_suspect = 38, n_ta_filler = 138, n_ta_reject = 206
)
fit <- fit_winter_2ht(counts, lineup_size = 6)

# Bootstrap CIs
boot_fit <- boot_winter_2ht(fit, nboot = 200, seed = 123)
print(boot_fit)
plot(boot_fit)

Description

Computes bootstrap confidence intervals around ANRI estimates using percentile method.

Usage

bootstrap_anri(
  data,
  confidence_bins,
  choosers_only = TRUE,
  lineup_size = 6,
  n_bootstrap = 1000,
  conf_level = 0.95,
  seed = NULL,
  confidence_scale = c("auto", "0-1", "0-100"),
  innocent_suspect = "auto"
)

Arguments

Details

Bootstrap resampling procedure: 1. Resample observations with replacement 2. Compute ANRI for each bootstrap sample 3. Construct percentile-based confidence interval

The percentile method uses the empirical quantiles of the bootstrap distribution. For a 95

Value

A list containing:

• anri: Point estimate of ANRI

• nri: Point estimate of NRI

• ci_lower: Lower bound of bootstrap CI

• ci_upper: Upper bound of bootstrap CI

• conf_level: Confidence level used

• n_bootstrap: Number of bootstrap replications

• bootstrap_distribution: Vector of bootstrap ANRI values

References

Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Bootstrap. Chapman & Hall/CRC.

Examples

set.seed(123)
n <- 200
data <- data.frame(
  target_present = rep(c(TRUE, FALSE), each = n / 2),
  identification = sample(c("suspect", "filler", "reject"), n,
                          replace = TRUE, prob = c(0.5, 0.25, 0.25)),
  confidence = round(runif(n, 0, 100))
)
boot_result <- bootstrap_anri(data, confidence_bins = seq(0, 100, 20),
                              n_bootstrap = 200, seed = 1)
c(boot_result$ci_lower, boot_result$ci_upper)

Description

Computes Bayesian posterior distributions for the calibration statistic C, over/underconfidence (O/U), and the Normalized Resolution Index (NRI) by placing independent Beta priors on the accuracy rate in each confidence bin.

Usage

calibration_bayes(
  data,
  confidence_bins = NULL,
  choosers_only = TRUE,
  lineup_size = 6,
  alpha = 0.5,
  S = 10000,
  credible_mass = 0.95,
  confidence_scale = c("auto", "0-1", "0-100"),
  innocent_suspect = "auto"
)

Arguments

Details

For each confidence bin $j$ with $n_j$ responses and $k_j$ correct identifications, the posterior accuracy is:

$a_j \mid \mathbf{n} \sim \mathrm{Beta}(k_j + \alpha,\; n_j - k_j + \alpha).$

For each posterior draw $s$, the calibration statistics are computed:

$C^{(s)} = \sum_j \frac{n_j}{N}(c_j - a_j^{(s)})^2$

$OU^{(s)} = \bar{c} - \sum_j \frac{n_j}{N} a_j^{(s)}$

$NRI^{(s)} = \frac{\sum_j \frac{n_j}{N}(a_j^{(s)} - \bar{a}^{(s)})^2}{\bar{a}^{(s)}(1-\bar{a}^{(s)})}$

where $c_j$ is the mean confidence (proportion scale) in bin $j$ and $\bar{c}$ is the overall mean confidence. This model assumes exchangeable Bernoulli correctness within each bin. In experiments with fixed TP and TA totals, conditioning on the number of identifications in a bin does not generally give a binomial correct count. These intervals are therefore conditional working-model summaries, rather than automatically exact posterior inference for that fixed-arm design. If innocent IDs are estimated from fillers, the fractional effective counts produce a working Beta posterior, not the exact likelihood for observed designated-suspect counts. Its intervals do not propagate all uncertainty in that estimation. Bin frequencies and mean confidence are conditioned on.

Value

An object of class "calibration_bayes" containing:

C_draws, OU_draws, NRI_draws

Posterior draws of each statistic.

C_mean, C_median, C_ci

Posterior mean, median, and credible interval for C.

OU_mean, OU_median, OU_ci

Same for O/U.

NRI_mean, NRI_median, NRI_ci

Same for NRI.

point_estimates

Frequentist C, OU, NRI from make_calibration_data().

bin_data

Per-bin summary (n, n_correct, mean_confidence, accuracy).

prior_alpha, S, credible_mass, n_total

Metadata.

References

Juslin, P., Olsson, N., & Winman, A. (1996). Calibration and diagnosticity of confidence in eyewitness identification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(5), 1304-1316.

Brewer, N., & Wells, G. L. (2006). The confidence-accuracy relationship in eyewitness identification. Journal of Experimental Psychology: Applied, 12(1), 11-30.

See Also

make_calibration_data, esize_T_bayes

Examples

set.seed(42)
n <- 200
conf  <- sample(c(50, 70, 90), n, replace = TRUE)
tp    <- sample(c(TRUE, FALSE), n, replace = TRUE)
ident <- ifelse(tp, "suspect", sample(c("suspect","filler","reject"), n, replace=TRUE))
df    <- data.frame(target_present = tp, identification = ident, confidence = conf)
res   <- calibration_bayes(df, confidence_bins = c(0, 60, 80, 100))
print(res)
plot(res)

Description

Verifies that all required Python packages are installed and reports their versions.

Usage

check_python_deps(verbose = TRUE)

Arguments

Value

A tibble with package names, installed status, and versions. Its "face_mesh" attribute says whether get_face_landmarks() can run: "solutions", "tasks" or "unavailable".

Examples

## Not run: 
check_python_deps()

## End(Not run)

Description

Reports whether Python and pyWitness are available without initializing Python by default.

Usage

check_pywitness_deps(
  envname = .pywitness_default_env,
  initialize = FALSE,
  verbose = TRUE
)

Arguments

Value

A one-row tibble with environment and engine status, including whether the imported version encodes the audited revision when initialize = TRUE.

Examples

check_pywitness_deps()
## Not run: 
check_pywitness_deps(initialize = TRUE)

## End(Not run)

Description

Function for getting chi-squared value for homogeneity of diagnosticity ratios

Usage

chi_diag(df)

Arguments

Details

• To compute linedf, use the diag_param helper function.

• To compute df, apply ln_diag_ratio, var_lnd & d_weights functions to linedf, then bind results into one dataframe (see Examples)

• The order in which the estimates are bound together (i.e., their position in the dataframe) is important, and should always be as follows: row 1: var, row 2: lnd, row 3: wi.

Value

Chi squared estimate of homogeneity of diagnosticity ratios for k independent lineups

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L., Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

#Target present data:
A <- rep(1:6, length.out = 100)
B <- rep(1:5, length.out = 70)
C <- rep(1:4, length.out = 20)
lineup_pres_list <- list(A, B, C)
rm(A, B, C)


#Target absent data:
A <- rep(6:1, length.out = 100)
B <- rep(5:1, length.out = 70)
C <- rep(4:1, length.out = 20)
lineup_abs_list <- list(A, B, C)
rm(A, B, C)

# Suspect position for each TP/TA pair
pos_list <- c(3, 2, 1)

#Nominal size:
k <- c(6, 5, 4)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

#Get ln(d), variance of ln(d) & d weights:
ratio <- ln_diag_ratio(linedf, correction = FALSE)
var <- var_lnd(linedf)
wi <- d_weights(linedf)

#Bind estimates into one df of 3 rows & x observations
#(see Details above)
df <- t(cbind(var, ratio, wi))

#Call:
chi_diag(df)

Description

Derives an identification outcome — suspect identification, foil identification, or rejection — from the raw record of which lineup member each witness actually chose.

Usage

classify_lineup_choices(
  data,
  member_col = "lineup_member",
  suspect_col = "suspect_pos",
  into = "identification",
  k = NULL,
  overwrite = FALSE
)

Arguments

Details

Lineup data should record which member a witness chose, not merely whether the choice happened to fall on the suspect. Foils are not interchangeable: they differ in how plausible they are, and a foil that no witness ever chooses is not a functioning alternative to the suspect. Storing only a collapsed "suspect / filler / reject" label discards the information needed to see that.

This function treats the collapsed label as derived. Raw data keeps the chosen position; classify_lineup_choices() produces the label on demand, so both are available and the two cannot drift apart.

The outcome is "suspect" when the chosen member is the suspect, "reject" when no member was chosen, and "filler" otherwise. These are the labels used throughout the package, so the result can be passed straight to make_roc, make_cac and the other analysis functions.

Value

data with the derived outcome column added. If k was supplied, the "null_foils" attribute gives the positions that no witness chose.

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

See Also

lineup_table for a tabulation that shows null foils, and simulate_lineup_data, which records lineup_member directly.

Examples

# Raw choices from an 8-person lineup in which the suspect stands at
# position 3; 0 records a rejection
trials <- data.frame(
  lineup_member = c(3, 0, 5, 3, 1, 0, 3, 7),
  suspect_pos   = 3
)

classify_lineup_choices(trials, k = 8)

# A suspect position common to every trial can be given as a number
classify_lineup_choices(
  data.frame(lineup_member = c(2, 0, 4)),
  suspect_col = 2
)

Description

Computes ANRI for two groups and tests whether they differ using bootstrap confidence intervals for the difference.

Usage

compare_anri(
  data,
  group_var,
  confidence_bins,
  choosers_only = TRUE,
  lineup_size = 6,
  n_bootstrap = 1000,
  conf_level = 0.95,
  seed = NULL,
  confidence_scale = c("auto", "0-1", "0-100"),
  innocent_suspect = "auto"
)

Arguments

Details

This function: 1. Computes ANRI separately for each group 2. Bootstraps each group independently 3. Computes bootstrap distribution of the difference 4. Tests H0: ANRI_1 = ANRI_2 using CI for difference

**Interpretation**:

• Positive difference: Group 1 has higher resolution

• Negative difference: Group 2 has higher resolution

• CI excludes 0: Statistically significant difference

Value

A list containing:

• anri_group1: ANRI for first group

• anri_group2: ANRI for second group

• difference: Point estimate of difference (group1 - group2)

• ci_lower: Lower bound of CI for difference

• ci_upper: Upper bound of CI for difference

• significant: Whether difference is significant (CI excludes 0)

• group_names: Names of the two groups

• bootstrap_results_group1: Bootstrap object for group 1

• bootstrap_results_group2: Bootstrap object for group 2

Examples

set.seed(123)
n <- 300
data <- data.frame(
  target_present = rep(c(TRUE, FALSE), each = n / 2),
  identification = sample(c("suspect", "filler", "reject"), n,
                          replace = TRUE, prob = c(0.5, 0.25, 0.25)),
  confidence = round(runif(n, 0, 100)),
  condition = rep(c("sequential", "simultaneous"), times = n / 2)
)
cmp <- compare_anri(data, group_var = "condition",
                    confidence_bins = seq(0, 100, 20),
                    n_bootstrap = 100, seed = 1)
cmp$difference
c(cmp$ci_lower, cmp$ci_upper)

Description

Computes average DPP for two lineup procedures and tests their difference with a nonparametric bootstrap, as Smith et al. (2019) did.

Usage

compare_dpp(
  data_proc_a,
  data_proc_b,
  lineup_size = 6,
  alpha = NULL,
  beta = NULL,
  n_bootstrap = 1000,
  conf_level = 0.95,
  seed = NULL,
  innocent_suspect = "auto"
)

Arguments

Details

Lower DPP indicates better performance, so a negative difference means procedure A is better. Each bootstrap replicate resamples target-present and target-absent trials separately within each procedure, so every replicate keeps the original design, and the two procedures are treated as independent samples. The innocent-suspect design and the confidence criteria are determined once on each full dataset and reused in every replicate, so every replicate averages over the same criteria.

Value

An object of class "lineup_dpp_comparison", a list containing:

• dpp_a: Average DPP for procedure A

• dpp_b: Average DPP for procedure B

• dpp_difference: DPP_A - DPP_B (negative = A is better)

• se_difference: Bootstrap standard error of the difference

• ci_difference: Percentile bootstrap interval for the difference

• boot_differences: Bootstrap distribution of the difference

• dpp_obj_a, dpp_obj_b: Full make_dpp() objects

• n_bootstrap, conf_level: Settings used

References

Smith, A. M., Lampinen, J. M., Wells, G. L., Smalarz, L., & Mackovichova, S. (2019). Deviation from perfect performance measures the diagnostic utility of eyewitness lineups but partial area under the ROC curve does not. Journal of Applied Research in Memory and Cognition, 8(1), 50-59. doi:10.1016/j.jarmac.2018.09.003

Examples

data(lineup_example)
# Compare two halves of the example data (odd vs even rows)
odd <- seq(1, nrow(lineup_example), by = 2)
# 50 resamples keep the example quick; use 1000 or more in an analysis
cmp <- compare_dpp(lineup_example[odd, ], lineup_example[-odd, ],
                   n_bootstrap = 50, seed = 1)
cmp

Description

A row-resampling statistic for two columns of lineup choices.

Usage

compare_eff_sizes.boot(linedf, d)

Arguments

Details

This statistic applies the same row indices to both columns. Using it with an ordinary row bootstrap therefore resamples paired observations. For independent lineup samples use effsize_compare, which resamples each sample independently. Bootstrap intervals remain approximations; inclusion or exclusion of zero does not establish calibration in small or nearly uniform samples.

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

# Two independent lineups of 6 members, 50 mock witnesses each
set.seed(1)
linedf <- data.frame(lineup1 = sample(1:6, 50, replace = TRUE),
                     lineup2 = sample(1:6, 50, replace = TRUE))
# Difference in effective size for the observed data
compare_eff_sizes.boot(linedf, seq_len(nrow(linedf)))
# Resample the independent lineups separately
effsize_compare(linedf, R = 100)

Description

Performs a chi-squared difference test comparing a less constrained (free) MAX SDT model to a more constrained (nested) model. Both models must be fitted to the same data.

Usage

compare_max_sdt(free, constrained)

Arguments

Details

The chi-squared difference statistic is:

$\Delta\chi^2 = \chi^2_{\text{constrained}} - \chi^2_{\text{free}}$

with degrees of freedom equal to the difference in the number of free parameters. A significant result indicates that the constraint worsens fit. The fits must use identical counts and lineup sizes, must have converged, and the constrained model must impose every constraint in the free model plus at least one additional constraint.

Value

An object of class "max_sdt_compare" with the test result.

See Also

fit_max_sdt

Examples

# Each two-condition fit takes a few seconds
fit_free <- fit_max_sdt(
  n_hit=69, n_tp_choose=82, n_fa=64, N_tp=96, N_ta=106,
  n_hit_2=68, n_tp_choose_2=78, n_fa_2=42, N_tp_2=96, N_ta_2=96
)
fit_eqd <- fit_max_sdt(
  n_hit=69, n_tp_choose=82, n_fa=64, N_tp=96, N_ta=106,
  n_hit_2=68, n_tp_choose_2=78, n_fa_2=42, N_tp_2=96, N_ta_2=96,
  constrain_d = TRUE
)
compare_max_sdt(fit_free, fit_eqd)

Description

Fits and compares multiple models (2-HT, EIG, Full ROC) to the same lineup identification dataset, providing a side-by-side descriptive table.

Usage

compare_models(
  data,
  models = c("2ht", "eig", "fullroc"),
  lineup_size = 6,
  prior_guilt = 0.5,
  confidence_bins = NULL,
  show_warnings = FALSE,
  ...
)

Arguments

Details

This function provides a unified interface for fitting and comparing multiple eyewitness identification models. It automatically handles different data requirements and output formats across models.

**Models Compared:**

• **2-HT (Winter et al., 2022)**: Multinomial processing tree model with parameters for detection (dP, dA), bias (b), and guessing (g). Provides AIC/BIC for comparison only with other likelihood models fit to the same observations and outcome representation.

• **EIG (Starns et al., 2023)**: Information-theoretic measure of evidentiary value. Higher values indicate more diagnostic procedures.

• **Full ROC (Smith & Yang, 2020)**: Uses ALL responses to compute investigator discriminability. AUC ranges from 0 to 1; 0.5 represents chance under a fixed ordering.

**Interpretation:**

• AIC/BIC describe the 2-HT likelihood fit but cannot be compared with EIG or full-ROC AUC

• Use EIG for comparing procedure diagnosticity (higher is better)

• Use Full ROC AUC for investigator discriminability (higher is better)

Value

An object of class "model_comparison" containing:

• comparison_table: Dataframe comparing model fits

• fitted_models: List of fitted model objects

• best_model: Always NULL. The included methods estimate different quantities and cannot be ranked by a common information criterion.

• data: The input data

• models_fit: Character vector of models successfully fit

References

Winter, K., Menne, N. M., Bell, R., & Buchner, A. (2022). Experimental validation of a multinomial processing tree model for analyzing eyewitness identification decisions. Scientific Reports, 12, 15571.

Starns, J. J., Cohen, A. L., & Rotello, C. M. (2023). A complete method for assessing the effectiveness of eyewitness identification procedures: Expected information gain. Psychological Review, 130(3), 677–719.

Smith, A. M., Yang, Y., & Wells, G. L. (2020). Distinguishing between investigator discriminability and eyewitness discriminability. Perspectives on Psychological Science, 15(3), 589-607.

Examples

data(lineup_example)
# Fit all models
comparison <- compare_models(lineup_example)
print(comparison)
summary(comparison)

# Fit specific models
comparison <- compare_models(lineup_example, models = c("2ht", "eig"))

# Access individual fitted models
comparison$fitted_models$`2ht`
comparison$fitted_models$eig

Description

Computes the difference (x minus y) on every Multi-d' index, for example simultaneous versus sequential lineups, or high- versus low-similarity lineups. When both objects carry bootstrap draws, standard errors, percentile intervals and normal-approximation p-values are given for each difference. The two conditions are treated as independent samples: the standard error is the square root of the sum of the two bootstrap variances, and the interval is taken from differences between randomly re-paired draws, so the result does not depend on whether the two fits used the same seed.

Usage

compare_multid(x, y, labels = c("x", "y"), seed = NULL)

Arguments

Value

A data frame of class "multid_comparison".

Examples

# Simultaneous vs sequential lineups, Lee & Penrod's (2019) Table 3
sim <- make_multid(counts = c(G = 52, Fp = 25, Rp = 24, I = 28, Fa = 26, Ra = 46),
                   correction = "none")
sqn <- make_multid(counts = c(G = 44, Fp = 19, Rp = 39, I = 15, Fa = 17, Ra = 68),
                   correction = "none")
compare_multid(sim, sqn, labels = c("simultaneous", "sequential"))

Description

Statistically compares partial Area Under the Curve (pAUC) values between two lineup conditions using bootstrap-based standard errors and z-tests. Follows the methodology from pyWitness (Mickes et al., 2024).

Usage

compare_pauc(
  data1,
  data2,
  lineup_size = 6,
  max_false_id_rate = NULL,
  n_bootstrap = 2000,
  conf_level = 0.95,
  seed = NULL,
  label1 = "Condition 1",
  label2 = "Condition 2",
  innocent_suspect = "auto"
)

Arguments

Details

This function implements an approximate statistical test for comparing ROC curves between two conditions (e.g., simultaneous vs. sequential lineups, different retention intervals, etc.).

**Method:**

1. Computes pAUC for each condition (optionally up to a maximum false ID rate)

2. Uses bootstrap resampling to estimate standard errors

3. Computes Z-statistic: Z = (pAUC1 - pAUC2) / SE(pAUC1 - pAUC2)

4. Calculates p-value from standard normal distribution

The two datasets are treated as independent conditions. This function does not implement a paired or clustered bootstrap.

**Interpretation:**

• Positive pAUC difference: Condition 1 has better discriminability

• Negative pAUC difference: Condition 2 has better discriminability

• p < 0.05: Significant difference between conditions

The independent-sample bootstrap estimates uncertainty in an empirical partial-area functional; the final comparison uses a normal/Wald interval. It is approximate and can be liberal in finite samples. A data-selected cutoff, sparse high-confidence cells, or resamples that do not reach the cutoff require additional caution. It is not an exact discrimination test.

Value

An object of class "pauc_comparison" containing:

• pauc1: pAUC for condition 1

• pauc2: pAUC for condition 2

• pauc_diff: Difference in pAUC (pauc1 - pauc2)

• se_pauc1: Bootstrap standard error for pAUC1

• se_pauc2: Bootstrap standard error for pAUC2

• se_diff: Standard error of the difference

• z_score: Z-statistic for difference test

• p_value: Two-tailed p-value

• ci_diff: Confidence interval for difference

• roc1: ROC object for condition 1

• roc2: ROC object for condition 2

• max_false_id_rate: Cutoff used for pAUC

• label1, label2: Condition labels

• n_bootstrap: Number of bootstrap samples used

References

Mickes, L., Seale-Carlisle, T. M., Chen, X., & Boogert, S. (2024). pyWitness 1.0: A python eyewitness identification analysis toolkit. Behavior Research Methods, 56, 1533-1550.

Wixted, J. T., & Mickes, L. (2012). The field of eyewitness memory should abandon probative value and embrace receiver operating characteristic analysis. Perspectives on Psychological Science, 7(3), 275-278.

Examples

# Compare two lineup procedures (halves of the example data)
data(lineup_example)
odd <- seq(1, nrow(lineup_example), by = 2)
comparison <- compare_pauc(
  lineup_example[odd, ],
  lineup_example[-odd, ],
  label1 = "Procedure A",
  label2 = "Procedure B",
  n_bootstrap = 200,
  seed = 123
)

print(comparison)
plot(comparison)

Description

Performs a large-sample z-test comparing d' (or criterion or ln beta) between two independent conditions using summary counts only.

Usage

compare_sdt_summary(
  hits_a,
  fas_a,
  misses_a,
  cr_a,
  hits_b,
  fas_b,
  misses_b,
  cr_b,
  metric = c("dprime", "c", "ln_beta"),
  method = c("miller", "gourevitch", "bootstrap"),
  correction = c("loglinear", "half", "none"),
  nboot = 1000,
  seed = NULL
)

Arguments

Value

A list with estimates, standard error, z, and p-value.

Examples

cmp <- compare_sdt_summary(hits_a = 70, fas_a = 20, misses_a = 30, cr_a = 80,
                           hits_b = 60, fas_b = 35, misses_b = 40, cr_b = 65,
                           metric = "dprime")
cmp$z
cmp$p_value

Description

Main wrapper function to compute and plot utility comparison between two lineup procedures.

Usage

compare_utility(
  data_proc_a,
  data_proc_b,
  base_rate_grid = seq(0.01, 0.99, 0.01),
  utility_matrix = c(tp = 1, fn = -0.5, fp = -2, tn = 0.5),
  utility_type = c("max", "avg", "all"),
  show_plot = TRUE,
  innocent_suspect = "auto",
  ...
)

Arguments

Value

A list containing difference curve data and plot

Examples

data(lineup_example)
odd <- seq(1, nrow(lineup_example), by = 2)
comparison <- compare_utility(lineup_example[odd, ],
                              lineup_example[-odd, ],
                              base_rate_grid = seq(0.1, 0.9, 0.1),
                              utility_type = "max")
print(comparison)

Description

Computes ANRI, a bias-corrected version of NRI following Yaniv et al. (1991). ANRI adjusts NRI for the number of confidence bins and sample size, providing more accurate estimates especially when J (bins) is small relative to N.

Usage

compute_anri(
  data,
  confidence_bins,
  choosers_only = TRUE,
  lineup_size = 6,
  confidence_scale = c("auto", "0-1", "0-100"),
  innocent_suspect = "auto"
)

Arguments

Details

ANRI corrects NRI for small-sample and small-bin-count bias:

$NRI = \frac{\frac{1}{N} \sum_{j=1}^{J} n_j (a_j - \bar{a})^2}{\bar{a}(1-\bar{a})}$

$ANRI = \frac{N \cdot NRI - J + 1}{N - J + 1}$

Where:

• N = total sample size

• J = number of bins

• n_j = sample size in bin j

• a_j = accuracy (proportion correct) in bin j

• a_bar = overall accuracy

**When to use ANRI vs NRI:**

• Use ANRI when J is small (e.g., 3-5 bins)

• Use ANRI when N is modest (e.g., < 200)

• Use ANRI when comparing across different numbers of bins

• NRI is asymptotically unbiased as N approaches infinity

ANRI provides a less biased estimate of the population resolution, making it more appropriate for hypothesis testing and group comparisons. When target-absent suspect identifications are estimated from filler choices, N and the bin counts are effective (possibly fractional) counts from make_calibration_data(), so the correction is an approximation. See make_calibration_data for the designated-suspect distinction.

Value

A list containing:

• anri: Adjusted Normalized Resolution Index

• nri: Original Normalized Resolution Index (unadjusted)

• n_total: Total sample size

• n_bins: Number of confidence bins

• calibration_data: Per-bin accuracy and sample sizes

• overall_accuracy: Mean accuracy

References

Yaniv, I., Yates, J. F., & Smith, J. E. K. (1991). Measures of discrimination skill in probabilistic judgment. Psychological Bulletin, 110(3), 611-617.

Juslin, P., Olsson, N., & Winman, A. (1996). Calibration and diagnosticity of confidence in eyewitness identification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(5), 1304-1316.

Examples

set.seed(123)
n <- 200
data <- data.frame(
  target_present = rep(c(TRUE, FALSE), each = n / 2),
  identification = sample(c("suspect", "filler", "reject"), n,
                          replace = TRUE, prob = c(0.5, 0.25, 0.25)),
  confidence = round(runif(n, 0, 100))
)
result <- compute_anri(data, confidence_bins = seq(0, 100, 20))
result$anri

Description

Main wrapper function to compute and plot DPP analysis.

Usage

compute_dpp(
  data,
  lineup_size = 6,
  show_plot = TRUE,
  alpha = NULL,
  beta = NULL,
  innocent_suspect = "auto",
  ...
)

Arguments

Value

An object of class "lineup_dpp": the make_dpp() result plus the plot.

Examples

data(lineup_example)
dpp_result <- compute_dpp(lineup_example, show_plot = FALSE)
print(dpp_result)

Description

Computes Expected Information Gain for lineup identification procedures following Starns et al. (2023). EIG quantifies the evidentiary value of witness responses using information theory.

Usage

compute_eig(
  eig_data,
  prior_guilt = 0.5,
  confidence_bins = NULL,
  lineup_size = 6,
  innocent_suspect = "auto"
)

Arguments

Details

EIG measures how much information (in bits) a witness response provides about guilt vs. innocence. It is computed as:

$EIG = \sum_x p(x) \times [H(prior) - H(p(guilty|x))]$

where:

• x = response category (e.g., "suspect_high_confidence")

• p(x) = probability of that response

• H() = Shannon entropy

• p(guilty|x) = posterior probability of guilt given response x

Higher EIG values indicate more diagnostic procedures. EIG = 0 means no information gain (responses don't distinguish guilty from innocent). The maximum EIG is the prior entropy; it is 1 bit only at prior guilt 0.5.

Value

A list of class "lineup_eig" containing:

• eig: Expected Information Gain in bits

• response_data: Dataframe with information gain for each response category

• prior_guilt: Prior probability used

• prior_entropy: Entropy of prior (baseline uncertainty)

• n_guilty: Number of target-present lineups

• n_innocent: Number of target-absent lineups

References

Starns, J. J., Cohen, A. L., & Rotello, C. M. (2023). A complete method for assessing the effectiveness of eyewitness identification procedures: Expected information gain. Psychological Review, 130(3), 677–719.

Examples

# Compute EIG with binned confidence
data(lineup_example)
eig_result <- compute_eig(lineup_example,
                          prior_guilt = 0.5,
                          confidence_bins = c(0, 60, 80, 100))
print(eig_result)

Description

Converts cosine distance to cosine similarity in [-1, 1].

Usage

cosine_to_similarity(distance)

Arguments

Value

Numeric cosine similarity score(s), where 1 means the same direction, 0 means orthogonal, and -1 means opposite directions.

Examples

# Distance of 0 -> similarity of 1
cosine_to_similarity(0)  # Returns 1

# Distance of 0.5 -> similarity of 0.5
cosine_to_similarity(0.5)  # Returns 0.5

Description

Generate example lineup identification data in standardized format. Useful for testing, demonstrations, and learning r4lineups functions.

Usage

create_example_lineup_data(
  n_trials = 100,
  prop_target_present = 0.5,
  include_confidence = TRUE,
  include_response_time = FALSE,
  seed = NULL
)

Arguments

Value

A lineup_data object with realistic example data

Examples

# Basic example data
example_data <- create_example_lineup_data(n_trials = 200, seed = 123)
print(example_data)

# Use in analysis
roc <- make_roc(example_data, lineup_size = 6)

Description

Function for computing pooled estimator from a set of k diagnosticity ratios

Usage

d_bar(df)

Arguments

Details

. The order in which the estimates are bound together (i.e., their position in the dataframe) is important, and should always be as follows:

• row 1: var

• row 2: lnd

• row 3: wi

Value

Mean diagnosticity ratio for k independent lineups

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

#Target present data:
A <- rep(1:6, length.out = 100)
B <- rep(1:5, length.out = 70)
C <- rep(1:4, length.out = 20)
lineup_pres_list <- list(A, B, C)
rm(A, B, C)


#Target absent data:
A <- rep(6:1, length.out = 100)
B <- rep(5:1, length.out = 70)
C <- rep(4:1, length.out = 20)
lineup_abs_list <- list(A, B, C)
rm(A, B, C)

# Suspect position for each TP/TA pair
pos_list <- c(3, 2, 1)

#Nominal size:
k <- c(6, 5, 4)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

#Compute ln(d):
ratio <- ln_diag_ratio(linedf, correction = FALSE)

#Compute variance of ln(d):
var <- var_lnd(linedf)

#Compute weights for pooled estimator:
wi <- d_weights(linedf)

#Bind ln(d), variance of ln(d) and weights into one df (of 3 rows & x observations)
#(see Details above):
df <- t(cbind(var, ratio, wi))

#Call:
d_bar(df)

Description

Function to compute weights of each diagnosticity ratio for k lineup pairs

Usage

d_weights(linedf)

Arguments

Details

In order to obtain a pooled estimate of a set of diagnosticity ratios, we use a weight for each ratio that is equal to the inverse of its variance.

To get linedf, use the diag_param helper function

diag_param returns a dataframe containing the following:

• n11: Number of mock witnesses who identified the suspect in the target present condition

• n21: Number of mock witnesses who did not identify the suspect in the target present condition

• n12: Number of mock witnesses who identified the suspect in the target absent condition

• n22: Number of mock witnesses who did not identify the suspect in the target absent condition

Value

A dataframe with one column containing weights for each pair of lineups for which diagnosticity ratio is being calculated.

References

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Examples

#Target present data:
A <- rep(1:6, length.out = 100)
B <- rep(1:5, length.out = 70)
C <- rep(1:4, length.out = 20)
lineup_pres_list <- list(A, B, C)
rm(A, B, C)


#Target absent data:
A <- rep(6:1, length.out = 100)
B <- rep(5:1, length.out = 70)
C <- rep(4:1, length.out = 20)
lineup_abs_list <- list(A, B, C)
rm(A, B, C)

# Suspect position for each TP/TA pair
pos_list <- c(3, 2, 1)

#Nominal size:
k <- c(6, 5, 4)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

#Call:
wi <- d_weights(linedf)

Description

Checks that number of lineup choices contained in a vector is accurate

Usage

datacheck1(lineup_vec, k)

Arguments

Details

This function ensures that a non-selected lineup member is not accidentally omitted from the dataframe due to lack of selection by all mock witnesses. It functions as a check that the total number of lineup members is accurate.

Examples

# Choices of 50 mock witnesses to a 6-member lineup: passes silently
set.seed(1)
lineup_vec <- sample(1:6, 50, replace = TRUE)
datacheck1(lineup_vec, 6)

Description

Checks that number of lineup choices contained in a vector is accurate

Usage

datacheck2(lineup_pres, lineup_abs, k1, k2)

Arguments

Details

This function ensures that a non-selected lineup member is not accidentally omitted from the dataframe due to lack of selection by all mock witnesses. It functions as a check that the total number of lineup members is accurate

Examples

# Target-present and target-absent lineups of 6 members each
set.seed(1)
lineup_pres <- sample(1:6, 50, replace = TRUE)
lineup_abs <- sample(1:6, 50, replace = TRUE)
datacheck2(lineup_pres, lineup_abs, 6, 6)

Description

Checks that number of lineup choices contained in a data table is accurate

Usage

datacheck3(lineup_tab, k)

Arguments

Details

This function ensures that a non-selected lineup member is not accidentally omitted from the data due to lack of selection by all mock witnesses. It functions as a check that the total number of lineup members is accurate.

Examples

# Table of choices from 50 mock witnesses to a 6-member lineup
set.seed(1)
lineup_tab <- table(sample(1:6, 50, replace = TRUE))
datacheck3(lineup_tab, 6)

Description

Detects all faces in an image and returns their bounding boxes and facial landmarks.

Usage

detect_faces(img_path, detector = "retinaface")

Arguments

Value

A list where each element represents a detected face with:

• facial_area - List with x, y, w, h coordinates

• landmarks - List with eye, nose, mouth positions (if available)

• confidence - Detection confidence score

Returns an empty list if no faces are detected.

Examples

## Not run: 
# Detect faces in an image
faces <- detect_faces("group_photo.jpg")

# Number of faces found
length(faces)

# Get bounding box of first face
faces[[1]]$facial_area

## End(Not run)

Description

This function calculates the parameters needed to calculate the diagnosticity ratio for several lineup pairs.

Usage

diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

Arguments

Details

• Lineup pairs consist of one lineup in which the target was present (TP) and one lineup in which the target was absent (TA).

• Each lineup pair must occupy corresponding positions in the TA and TP lists.

  Example:

  For a lineup pair A that consists of (1)TP lineup and (2)TA lineup: A(1) is the first vector in the TP list A(2) is the first vector in the TA list

• The order in which nominal size for each lineup pair is listed must also correspond with the positions of each respective lineup in the lineup lists (i.e., if lineup 1 has k = 6, then the first element of vector 'k' = 6)

• Data must be in a list format. This allows the function to compare lineups in which the number of choices and number of lineup members differs.

• TP and TA vectors may have different sample sizes; they are counted independently and are never compared element by element.

Value

Returns a dataframe containing:

• n11: Number of mock witnesses who identified the suspect in the target present condition

• n21: Number of mock witnesses who did not identify the suspect in the target present condition

• n12: Number of mock witnesses who identified the suspect in the target absent condition

• n22: Number of mock witnesses who did not identify the suspect in the target absent condition

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

#Target present data:
A <- rep(1:6, length.out = 100)
B <- rep(1:5, length.out = 70)
C <- rep(1:4, length.out = 20)
lineup_pres_list <- list(A, B, C)
rm(A, B, C)


#Target absent data:
A <- rep(6:1, length.out = 100)
B <- rep(5:1, length.out = 70)
C <- rep(4:1, length.out = 20)
lineup_abs_list <- list(A, B, C)
rm(A, B, C)

# Suspect position for each TP/TA pair
pos_list <- c(3, 2, 1)

#Nominal size:
k <- c(6, 5, 4)

#Call:
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

Description

Computes Tredoux's small-cell corrected diagnosticity ratio for one target-present/target-absent lineup pair.

Usage

diag_ratio_T(lineup_pres, lineup_abs, pos_pres, pos_abs, k1, k2)

Arguments

Details

The corrected estimator is:

$DR_T = \frac{(n_{TP,s} + 0.5)/(N_{TP} + 0.5)} {(n_{TA,s} + 0.5)/(N_{TA} + 0.5)},$

where $n_{TP,s}$ and $n_{TA,s}$ are suspect-selection counts in target-present and target-absent lineups, respectively, and $N_{TP}$ and $N_{TA}$ are the corresponding sample sizes.

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

#Data:
lineup_pres <- round(runif(100, 1, 6))
lineup_abs <- round(runif(70, 1, 5))
pos_pres <- 3
pos_abs <- 5

#Call:
diag_ratio_T(lineup_pres, lineup_abs, pos_pres, pos_abs, 6, 5)
diag_ratio_T(lineup_pres, lineup_abs, 3, 5, 6, 5)

Description

Computes a Bayesian posterior distribution for the Tredoux diagnosticity ratio using independent Beta-Binomial models for the target-present and target-absent suspect-selection rates.

Usage

diag_ratio_T_bayes(
  lineup_pres,
  lineup_abs,
  pos_pres,
  pos_abs,
  k1,
  k2,
  alpha = 0.5,
  S = 10000,
  credible_mass = 0.95,
  threshold = NULL
)

Arguments

Details

The target-present suspect-selection rate $p_{TP}$ and the target-absent rate $p_{TA}$ are modelled independently with Beta posteriors:

$p_{TP} \mid \mathbf{n} \sim \mathrm{Beta}(n_{TP,s} + \alpha,\; N_{TP} - n_{TP,s} + \alpha)$

$p_{TA} \mid \mathbf{n} \sim \mathrm{Beta}(n_{TA,s} + \alpha,\; N_{TA} - n_{TA,s} + \alpha)$

The posterior of the diagnosticity ratio is obtained by drawing $DR^{(s)} = p_{TP}^{(s)} / p_{TA}^{(s)}$ for each sample $s$. The default Jeffreys prior ($\alpha = 0.5$) is recommended for small samples. Unlike the continuity-corrected point estimate, the posterior handles zero cell counts naturally and provides direct probability statements such as $P(DR > 1 \mid \mathbf{n})$.

The posterior mean is computed exactly, not from the draws. Because the two rates are independent, $E[DR] = E[p_{TP}] \, E[1/p_{TA}]$, with $E[1/p_{TA}] = (a + b - 1)/(a - 1)$ for $p_{TA} \sim \mathrm{Beta}(a, b)$ when $a > 1$. When $a \le 1$ (for example, no target-absent suspect choices under the Jeffreys prior) the mean is infinite, and an average of the draws would not settle on any value as S grew. Report the median and interval instead. In diag_ratio_T_bayes_compare(), the mean difference is NA when both means are infinite.

Value

An object of class "diag_ratio_T_bayes" containing:

DR_draws

Numeric vector of length S: posterior draws of DR.

lnDR_draws

Log-transformed posterior draws.

posterior_mean, posterior_median

Posterior point estimates. The mean is exact, and is Inf when it does not exist (see Details).

credible_interval

Named two-element vector (lower, upper).

prior_alpha

Alpha value used.

n_tp, n_ta, S, credible_mass

Input metadata.

threshold, threshold_probs

Threshold and associated probabilities (or NULL).

References

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Wells, G. L., & Turtle, J. W. (1986). Eyewitness identification: The importance of lineup models. Psychological Bulletin, 99(3), 320-329.

See Also

diag_ratio_T, diag_ratio_T_bayes_compare, esize_T_bayes

Examples

set.seed(1)
lineup_pres <- round(runif(100, 1, 6))
lineup_abs  <- round(runif(80, 1, 6))
res <- diag_ratio_T_bayes(lineup_pres, lineup_abs, pos_pres = 3, pos_abs = 3,
                           k1 = 6, k2 = 6)
print(res)
plot(res)

Description

Fits independent Beta-Binomial models to two lineup pairs and returns the posterior distribution of the difference $\Delta = DR_A - DR_B$, together with $P(DR_A > DR_B \mid \mathbf{n})$.

Usage

diag_ratio_T_bayes_compare(
  pres_A,
  abs_A,
  pos_pres_A,
  pos_abs_A,
  k1_A,
  k2_A,
  pres_B,
  abs_B,
  pos_pres_B,
  pos_abs_B,
  k1_B,
  k2_B,
  alpha = 0.5,
  S = 10000,
  credible_mass = 0.95
)

Arguments

Value

An object of class "diag_ratio_T_bayes_compare".

See Also

diag_ratio_T_bayes

Examples

set.seed(1)
pres_A <- round(runif(100, 1, 6)); abs_A <- round(runif(80, 1, 6))
pres_B <- round(runif(100, 1, 6)); abs_B <- round(runif(80, 1, 6))
res <- diag_ratio_T_bayes_compare(pres_A, abs_A, 3, 3, 6, 6,
                                   pres_B, abs_B, 3, 3, 6, 6)
print(res)

Description

Computes Wells' diagnosticity ratio for one lineup pair

Usage

diag_ratio_W(lineup_pres, lineup_abs, pos_pres, pos_abs, k1, k2)

Arguments

References

Wells, G. L., & Lindsay, R. C. L. (1980).On estimating the diagnosticity of eyewitness nonidentifications.Psychological Bulletin, 88, 776-784.

Wells, G. L., & Turtle, J. W. (1986). Eyewitness identification: The importance of lineup models. Psychological Bulletin, 99, 320-329.

Examples

#Data:
lineup_pres <- round(runif(100, 1, 6))
lineup_abs <- round(runif(70, 1, 5))
pos_pres <- 3
pos_abs <- 5

#Call:
diag_ratio_W(lineup_pres, lineup_abs, pos_pres, pos_abs, 6, 5)
diag_ratio_W(lineup_pres, lineup_abs, 3, 5, 6, 5)

Description

Creates a visual display of the lineup faces arranged in a grid.

Usage

display_lineup(
  target_path,
  foil_paths,
  ncol = 3,
  scale = 150,
  show_labels = TRUE,
  highlight_target = TRUE
)

Arguments

Value

A magick image object that can be displayed or saved.

Examples

## Not run: 
# Display lineup
img <- display_lineup("target.jpg", paste0("foil", 1:5, ".jpg"))
print(img)

# Save to file
magick::image_write(img, "lineup_display.png")

## End(Not run)

Description

Distribution functions for the maximum of (m-1) independent standard normal filler signals, where m is the lineup size (suspect + fillers).

Usage

dmax_filler(x, lineup_size, log = FALSE)

pmax_filler(q, lineup_size, lower.tail = TRUE, log.p = FALSE)

qmax_filler(p, lineup_size, lower.tail = TRUE, log.p = FALSE)

rmax_filler(n, lineup_size)

Arguments

Value

Density, distribution, quantile, or random draws.

Examples

dmax_filler(0, lineup_size = 6)
pmax_filler(0, lineup_size = 6)
qmax_filler(0.5, lineup_size = 6)
rmax_filler(5, lineup_size = 6)

Description

A package-specific count of members whose choice-proportion intervals include chance guessing. This is distinct from Malpass' continuous effective size.

Usage

eff_size_per_foils(lineup_vec, target_pos, k, conf = 0.95, R = 1000)

Arguments

Details

Because the result is a count of members rather than a continuous quantity, it changes only in whole steps as conf varies, and it is flat across ranges of conf in which no member's interval crosses $1/k$. A value that does not move when conf is changed from, say, 0.95 to 0.99 is therefore expected behaviour and not a sign that the argument is being ignored. Widening the interval can only ever increase the count.

Intervals are computed by lineup_boot_allprop, and are not corrected for the fact that k of them are examined at once.

Value

A single integer: the number of lineup members whose bootstrapped confidence interval for the proportion choosing them includes the chance value $1/k$. These are the members that a mock witness chose at a rate not distinguishable from guessing.

References

Malpass, R S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5, 299-309.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
target_pos <- c(1, 2, 3, 4, 5, 6)

#Call:
eff_size_per_foils(lineup_vec, target_pos, 6)
eff_size_per_foils(lineup_vec, target_pos, conf = 0.95, 6)

Description

Function for comparing effective size of two independent lineups (Tredoux, 1998)

Usage

effsize_compare(linedf, R = 1000)

Arguments

Details

This function is a master function, calling other functions it needs, and reporting results in some detail Each lineup is resampled independently; row alignment has no sampling meaning. Confidence intervals are bootstrap approximations and can be unreliable for small or nearly uniform empirical choice distributions. Normal and BCa intervals can under-cover in finite concentrated samples; percentile intervals can be conservative near uniformity. None provides a distribution-free level-.05 test of equal population effective sizes.

Value

Effective size, significance level, and confidence intervals (95 normal theory, percentile & bias-corrected)

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

#Data:

lineup_vec1 <- round(runif(100, 1, 6))
lineup_vec2 <- round(runif(100, 1, 6))
linedf <- as.data.frame(cbind(lineup_vec1, lineup_vec2))

#Call:
x <- effsize_compare(linedf, R = 100)

Description

Calculates the distance or similarity between two face embedding vectors.

Usage

embedding_distance(
  emb1,
  emb2,
  metric = c("cosine", "euclidean", "euclidean_l2")
)

Arguments

Details

For ArcFace and other angular-margin trained models, cosine distance is recommended as it aligns with the training objective.

Cosine distance ranges from 0 (identical) to 2 (opposite). Typical thresholds for same-person verification:

• ArcFace + cosine: ~0.68

• Facenet512 + cosine: ~0.30

Value

Numeric distance value. Lower values indicate more similar faces.

Examples

## Not run: 
emb1 <- get_embedding("face1.jpg")$embedding
emb2 <- get_embedding("face2.jpg")$embedding

# Cosine distance (recommended)
embedding_distance(emb1, emb2, metric = "cosine")

# Euclidean distance
embedding_distance(emb1, emb2, metric = "euclidean")

## End(Not run)

Description

Computes a bootstrap distribution for effective size using either Tredoux's E' or Malpass's adjusted E.

Usage

esize_boot_dist(
  lineup_input,
  k,
  metric = c("tredoux", "malpass"),
  input_type = c("vector", "table"),
  R = 1000,
  unbiased = FALSE
)

Arguments

Details

Percentile or BCa intervals from the plug-in E' bootstrap are not valid tests of exact uniform fairness. The population maximum E'=k is a nonregular boundary. If the sample size is not a multiple of k, even the largest attainable empirical E' is below k, so those intervals have zero coverage at exact fairness. More bootstrap replicates cannot fix this.

Value

A numeric vector of bootstrap estimates, carrying an "n" attribute equal to the number of lineup choices resampled. With unbiased = TRUE, resamples in which every choice fell on a different member have an unbounded corrected estimate; they are retained as Inf, with a warning, and their number is recorded in an "n_undefined" attribute.

Examples

vec <- round(runif(200, 1, 6))
dist <- esize_boot_dist(vec, k = 6, metric = "tredoux", R = 500)
dist_corrected <- esize_boot_dist(vec, k = 6, R = 500, unbiased = TRUE)

Description

Function for computing Effective Size

Usage

esize_m(lineup_tab, k, both = FALSE)

Arguments

Details

Reduces the size of a lineup from a (corrected) nominal starting value by the degree to which members are, in sum, chosen below the level of chance expectation.

Value

The Tredoux-adjusted Malpass effective-size estimate. With both = TRUE, the original and adjusted estimates are also printed.

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

See Also

esize_T, whose “Small-sample bias” section describes a downward bias affecting plug-in effective-size estimators at small numbers of choices, and an optional correction.

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Call:
esize_m(lineup_vec, 6, both = TRUE)
esize_m(lineup_vec, 6)

Description

Base function for computing bootstrapped effective size

Usage

esize_m_boot(lineup_vec, d, k)

Arguments

Details

Function to call when bootstrap resampling using boot function (in package 'boot')

Value

The Tredoux-adjusted Malpass effective-size estimate for the resample.

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Get boot object:
bootobject <- boot::boot(lineup_vec, esize_m_boot, k = 6, R=1000)
bootobject

#To get confidence intervals:
cis <- boot::boot.ci(bootobject, conf = 0.95, type = "all", k = 6)

Description

Function for generating Tredoux's transformed index-of-diversity effective size.

Usage

esize_T(lineup_tab, unbiased = FALSE)

Arguments

Details

Tredoux (1998) proposed using Agresti and Agresti's (1978) index of diversity for lineup choices,

$I = 1 - \frac{\sum_i o_i^2}{N^2},$

where $o_i$ is the observed count for lineup member $i$ and $N$ is the total number of choices. The index is then transformed back to an effective-size scale:

$E' = \frac{1}{1-I} = \frac{N^2}{\sum_i o_i^2} = \frac{1}{\sum_i p_i^2}.$

This formula is algebraically equivalent to a reciprocal concentration form.

Small-sample bias

The expression above is a plug-in estimator: sample proportions are substituted for the unknown $p_i$. The plug-in concentration $\sum_i \hat p_i^2$ is biased upward. Its reciprocal $E'$ is biased downward for fair and near-fair lineups, but the direction is not guaranteed: when one member draws most choices the bias can be upward (two members, $p = (.9, .1)$, $N = 10$: mean 1.230 against a true 1.220, by exact enumeration). For $N$ choices spread evenly over $k$ members the bias is approximately

$E[\hat{E}'] \approx \frac{Nk}{N + k - 1}, \qquad \mathrm{bias} = -\frac{k(k-1)}{N + k - 1}.$

A perfectly fair six-person lineup assessed with 50 choices therefore returns about 5.47 rather than 6, and with 20 choices about 4.87. The bias is depends on both nominal size and the choice distribution; no single sample size makes it negligible for every lineup.

With unbiased = TRUE the function substitutes the unbiased estimator of the concentration, $\sum_i o_i(o_i - 1) / (N(N-1))$, and returns its reciprocal,

$\hat{E}'_{\mathrm{corr}} = \frac{N(N-1)}{\sum_i o_i(o_i - 1)}.$

The reciprocal of an unbiased estimate is not itself unbiased. For near-fair lineups this can reduce bias, with increased variability; in concentrated distributions it can overshoot (mean 1.269 in the two-member example). When fewer than two choices are available the correction is undefined and returns NA with a warning. When every choice is on a different member, the corrected reciprocal is Inf, retained with a warning. If $N \le k$, that event can have positive probability, so the corrected estimator can have an infinite sampling mean. It is not uniformly superior.

Which to use depends on what is being reported. For a description of the choice distribution actually observed, the plug-in value is the quantity of interest. For inference about the lineup that produced those choices, the framing of Tredoux (1998), the corrected form usually has less bias when choices are spread across members. Neither is uniformly better. Note that the other functions in this family (esize_T_boot, esize_T_ci_n, i_esize_T) use the plug-in form.

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Agresti, A., & Agresti, B. F. (1978). Statistical analysis of qualitative variation. Sociological Methodology, 9, 204-237.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L., Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
lineup_tab <- table(lineup_vec)

#Call:
e <- esize_T(lineup_tab)

Description

Fits a Dirichlet-Multinomial conjugate model to mock-witness choice counts and returns the posterior distribution of effective size E' (Tredoux, 1998).

Usage

esize_T_bayes(
  lineup_tab,
  alpha = 0.5,
  S = 10000,
  credible_mass = 0.95,
  threshold = NULL
)

Arguments

Details

Effective size E' = 1/sum(p_i^2) (Tredoux, 1998) is mathematically identical to the reciprocal of the Herfindahl-Hirschman concentration index (Herfindahl, 1950; Hirschman, 1945) and to the reciprocal of Simpson's (1949) concentration statistic lambda. The Bayesian approach estimates the latent choice-probability vector p rather than E' directly.

With mock-witness counts n = (n_1, ..., n_k) and a Dirichlet prior:

$p \sim \mathrm{Dirichlet}(\alpha_1, \ldots, \alpha_k),$

the posterior is conjugate:

$p \mid n \sim \mathrm{Dirichlet}(n_1 + \alpha_1, \ldots, n_k + \alpha_k).$

Each posterior draw of p is mapped to a draw of E' via E'(p) = 1/sum(p_i^2). The resulting posterior distribution of E' summarises uncertainty about effective lineup size given the observed choices.

Dirichlet samples are generated by normalising independent Gamma draws (base R only; no additional package dependencies).

For publication, a prior sensitivity analysis using at least alpha = 0.5, 1, and 0.1 is recommended. If the substantive conclusion changes across these priors, the mock-witness sample is likely too small to support a confident fairness conclusion.

Value

An object of class "esize_T_bayes" containing:

E_draws

Numeric vector of S posterior draws of E'.

posterior_mean

Posterior mean of E'.

posterior_median

Posterior median of E'.

credible_interval

Named 2-vector (lower, upper) giving the equal-tailed credible interval.

prior_alpha

The alpha vector used.

n

Total number of mock-witness choices.

k

Number of lineup positions.

S

Number of posterior draws.

credible_mass

Credible interval width.

threshold

Threshold value supplied, or NULL.

threshold_probs

List with P_below and P_above, or NULL when no threshold was supplied.

References

Herfindahl, O. C. (1950). Concentration in the US steel industry. Doctoral dissertation, Columbia University.

Hirschman, A. O. (1945). National power and the structure of foreign trade. University of California Press.

Simpson, E. H. (1949). Measurement of diversity. Nature, 163, 688.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

See Also

esize_T, esize_T_ci_n, esize_boot_dist, esize_T_bayes_compare

Examples

counts <- c(18, 7, 5, 4, 3, 3)
lineup_tab <- as.table(setNames(counts, 1:6))

# Default Jeffreys prior
result <- esize_T_bayes(lineup_tab)
print(result)

# With a fairness threshold
result2 <- esize_T_bayes(lineup_tab, threshold = 4)
print(result2)

# Plot the posterior
plot(result)

Description

Fits independent Dirichlet-Multinomial models to two lineup tables and returns the posterior distribution of the difference in Tredoux E'.

Usage

esize_T_bayes_compare(
  table_a,
  table_b,
  alpha = 0.5,
  S = 10000,
  credible_mass = 0.95
)

Arguments

Details

Independent Dirichlet-Multinomial posteriors are fitted for each lineup. The posterior of Delta = E'_A - E'_B is obtained by subtracting corresponding draws. P(E'_A > E'_B | data) is reported directly.

When the two lineups have different nominal sizes (different k), alpha must be a scalar; it is expanded independently for each lineup.

Value

An object of class "esize_T_bayes_compare" containing:

delta

Posterior draws of Delta = E'_A - E'_B.

posterior_mean

Posterior mean of Delta.

posterior_median

Posterior median of Delta.

credible_interval

Named 2-vector (lower, upper) for Delta.

P_A_greater

Posterior probability that E'_A > E'_B.

P_B_greater

Posterior probability that E'_B > E'_A.

result_a

The esize_T_bayes object for lineup A.

result_b

The esize_T_bayes object for lineup B.

credible_mass

Credible interval width.

S

Number of posterior draws.

References

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

See Also

esize_T_bayes, compare_eff_sizes.boot

Examples

counts_a <- c(20, 5, 5, 5, 5, 5)
counts_b <- c(10, 8, 8, 8, 8, 8)
tab_a <- as.table(setNames(counts_a, 1:6))
tab_b <- as.table(setNames(counts_b, 1:6))

result <- esize_T_bayes_compare(tab_a, tab_b)
print(result)
plot(result)

Description

Base function for generating bootstrapped Effective Size (Tredoux, 1998)

Usage

esize_T_boot(lineup_tab, d)

Arguments

Details

This is the statistic function passed to boot::boot() for bootstrapping Tredoux's E'. The data argument must contain individual lineup choices; d resamples mock witnesses rather than count-table cells. For a higher-level interface that accepts raw lineup vectors, see esize_boot_dist. Percentile and BCa intervals from this plug-in statistic cannot be used to test exact fairness E'=k; see the boundary warning in esize_boot_dist.

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L., Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

# Choices from 50 mock witnesses to a 6-member lineup
set.seed(1)
lineup_tab <- sample(1:6, 50, replace = TRUE)
# E' for the observed data
esize_T_boot(lineup_tab, seq_along(lineup_tab))

Description

Computes effective size E' (Tredoux, 1998) together with a confidence interval derived from the normal-theory variance formula in Tredoux (1998).

Usage

esize_T_ci_n(lineup_tab, alpha)

Arguments

Details

The variance of the diversity index I is estimated via Tredoux's (1998) closed-form expression:

$\mathrm{Var}(I) \approx \frac{4}{N}\left(\sum_i p_i^3 - \left(\sum_i p_i^2\right)^2\right).$

Normal quantiles are applied to I and the resulting bounds are back-transformed to the E' scale via $E' = 1/(1-I)$. For small samples or skewed distributions, all interval methods require qualification. In particular, plug-in bootstrap intervals also fail at exact uniform fairness; see esize_boot_dist. Bayesian credible intervals describe a stated posterior rather than guaranteeing fixed-parameter frequentist coverage.

Note that this concerns the interval. The point estimate itself is also biased downward near uniform choice distributions at small samples, although the direction is not universal, since $E'$ is a plug-in estimator; see the “Small-sample bias” section of esize_T. This function uses the plug-in form. This first-order interval is unreliable in small samples and at uniform or concentrated boundary distributions. A zero estimated variance yields a degenerate interval, not evidence of zero population uncertainty. An ordinary bootstrap is also not a general solution to these boundary problems.

References

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

Examples

#Data:
lineup_vec <- round(runif(100, 1, 6))
lineup_tab <- table(lineup_vec)

#Call:
e_ci <- esize_T_ci_n(lineup_tab, .95)

Description

Estimate decision criterion (gamma) and discriminability (d') from rejection rates in culprit-absent and culprit-present lineups.

Usage

estimate_msdt_params(pr_rej_I, pr_rej_G, lineup_size, eps = 1e-06)

msdt_gamma_from_rej(pr_rej_I, lineup_size, eps = 1e-06)

msdt_dprime_from_rej(pr_rej_G, gamma, lineup_size, eps = 1e-06)

Arguments

Details

This is a method-of-moments estimator that is exactly identified from the two rejection rates alone: Pr(REJ|I) fixes gamma, and the gap between culprit-absent and culprit-present rejection rates fixes d'. It is information-lossy: it ignores the composition of choices (suspect-ID vs filler-ID) and the spread of filler choices, which are the most diagnostic outcomes. If you have the full response breakdown (suspect/filler/reject counts for both lineup types), prefer the minimum-Pearson fitter fit_max_sdt or the 2-HT model (fit_winter_2ht), which use all outcome counts.

Value

A list with gamma and dprime.

See Also

fit_max_sdt for a full-outcome, minimum-Pearson fit; fit_winter_2ht for the 2-HT MPT model.

Examples

estimate_msdt_params(pr_rej_I = 0.25, pr_rej_G = 0.15, lineup_size = 6)

Description

Extracts d' (probit) or ln(OR) (logit) from SDT GLM/GLMM fits. Criterion is reported only when is_old was centered to -0.5/+0.5. With covariates these quantities are evaluated at zero/reference covariates; interactions can change them at other covariate values. GLMM coefficients are conditional latent-scale effects, not marginal population SDT measures. A logit coefficient is log odds ratio rather than normal-theory d-prime.

Usage

extract_sdt_metrics(model, link = NULL)

Arguments

Value

A list containing d' or ln(OR), criterion (if available), and metadata.

Examples

set.seed(123)
n <- 200
is_old <- rep(c(0, 1), each = n / 2)
said_old <- rbinom(n, 1, ifelse(is_old == 1, 0.7, 0.3))
df <- data.frame(is_old = is_old, said_old = said_old)
fit <- fit_sdt_glm(df, is_old = "is_old", said_old = "said_old")
metrics <- extract_sdt_metrics(fit)
metrics$estimate  # d' under the probit link

Description

Computes the similarity between two face images using deep learning embeddings. This is the core function for comparing a target face to a foil face.

Usage

face_similarity(
  img1_path,
  img2_path,
  model = "ArcFace",
  metric = "cosine",
  detector = "retinaface",
  return_embeddings = FALSE
)

Arguments

Details

This function wraps DeepFace's verify function but is designed for similarity measurement rather than identity verification. In lineup research, we typically want to measure how similar foils are to the target, not whether they are the same person.

The 'verified' field is the model's identity decision: whether the distance falls inside the model's threshold for "same person". In a lineup it is a prompt to inspect the two photographs, not a finding. Face recognition systems make false matches, and a match does not show that a foil is nonfunctioning for witnesses.

Value

A list containing:

• distance - The computed distance (lower = more similar)

• similarity - Similarity score in [-1,1] for cosine metric

• verified - Logical, TRUE if distance is below the model's threshold

• threshold - The verification threshold for this model/metric

• model - Model used

• metric - Metric used

• embeddings - (if return_embeddings=TRUE) List of both embeddings

Examples

## Not run: 
# Compare target face to a foil
result <- face_similarity("target.jpg", "foil1.jpg")

# Check similarity score
result$similarity  # 0.85 would mean very similar

# Inspect the model's same-identity flag; this is not a fairness judgment.
result$verified  # TRUE would indicate problematic similarity

## End(Not run)

Description

Fits confidence-binned Independent Observations, Ensemble/BEST-Rest, and Integration models through an explicitly installed pyWitness engine.

Usage

fit_lineup_models(
  data,
  models = c("independent", "ensemble", "integration"),
  lineup_size = 6L,
  confidence_bins = NULL,
  variance = c("equal", "unequal"),
  shared_variance = c("estimated", "zero"),
  starts = NULL,
  control = NULL,
  envname = .pywitness_default_env
)