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r4lineups (version 0.1.1)

Statistical Inference on Lineup Fairness

Description

Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? A lineup is supposed to consist of a suspect and a number of additional members, or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup measures are descriptive, in the first instance, but since the earliest articles in the literature researchers have recognised the importance of reasoning inferentially about them. This package contains functions to compute various properties of laboratory or police lineups, and is intended for use by researchers in forensic psychology and/or eyewitness testimony research. Among others, the r4lineups package includes functions for calculating lineup proportion, functional size, various estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity ratio, ROC curves for confidence x accuracy data and the degree of similarity of faces in a lineup.

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Version

Install

install.packages('r4lineups')

Monthly Downloads

168

Version

0.1.1

License

CC0

Maintainer

Colin Tredoux

Last Published

July 18th, 2018

Functions in r4lineups (0.1.1)

diag_ratio_W

Diagnosticity Ratio (Wells & Lindsay, 1980; Wells & Turtle, 1986)
face_sim

Compute similarity of faces in a lineup; experimental function
func_size.boot

Bootstrapped Functional Size
gen_boot_samples

Bootstrap resampling
lineup_boot_allprop

Confidence intervals for lineup proportion
eff_size_per_foils

Effective Size per Foils
gen_esize_m

Effective Size (across a dataframe)
lineup_prop_boot

Bootstrapped lineup proportion
rot_vector

Rotate vector
gen_esize_m_ci

Bootstrapped Confidence Intervals for Effective Size
show_lineup

Helper function
homog_diag_boot

Homogeneity of diagnosticity ratio with bootstrapped CIs
homog_diag

Master function: Homogeneity of diagnosticity ratio
gen_linevec

Lineup vector
mockdata

mockdata
gen_lineup_prop

Lineup proportion over dataframe
gen_boot_propci

Percentile of Bootstrapped Lineup Proportion
gen_boot_propmean_se

Descriptive statistics for bootstrapped lineup proportion
mickwick

Confidence & Accuracy data (Mickes & Wixted)
line73

line73
func_size

Functional Size
gen_boot_samples_list

Bootstrapped resampling
func_size_report

Functional Size with Bootstrapped Confidence Intervals
i_esize_T

I Component of Effective Size(Tredoux, 1998)
lineup_prop_vec

Lineup proportion
var_diag_ratio

Variance of diagnosticity ratio (Tredoux)
rep_index

Rep index
nortje2012

nortje2012
makevec_prop

Helper functions
lineup_prop_tab

Lineup proportion
var_lnd

Variance of ln of diagnosticity ratio
ln_diag_ratio

Ln of Diagnosticity Ratio
make_rocdata

Helper functions: Compute and plot ROC curve for lineup accuracy ~ confidence
make_roc

Compute and plot ROC curve for lineup accuracy ~ confidence
datacheck3

Helper function
datacheck1

Helper function
allprop

Lineup proportion for all lineup members
chi_diag

Chi-squared estimate of homogeneity of diagnosticity ratio
d_bar

Mean diagnosticity ratio for k lineup pairs
compare_eff_sizes.boot

Comparing Effective Size: Base function for bootstrapping
d_weights

Diagnosticity ratio weights
allfoil_cihigh

Confidence Intervals for Proportion
esize_T

Effective Size (Tredoux, 1998)
diag_ratio_T

Diagnosticty Ratio (Tredoux, 1998)
effsize_compare

Master Function: Comparing Effective Size
allfoilbias

Bias for each lineup member
diag_param

Parameters for diagnosticity ratio
datacheck2

Helper function
esize_T_boot

Bootstrapped Effective Size (Tredoux, 1998)
esize_m

Effective Size
esize_m_boot

Bootstrapped Effective Size
esize_T_ci_n

Effective Size with Confidence Intervals from Normal Theory (Tredoux, 1998)