Package {retestR}

Title:	Model-Based Anomaly Detection for Repeat Test-Takers
Version:	0.1.0
Description:	Flags repeat test-takers whose second-attempt performance departs from what a growth model predicts, using independent evidence sources: model-expected score gain (accounting for regression to the mean, time between attempts and remediation), differential performance on exposed versus new items (Sinharay, 2017, <doi:10.3102/1076998616673872>), and differential response speed under a lognormal response-time model (van der Linden, 2006, <doi:10.3102/10769986031002181>). Evidence is combined into a risk index calibrated by parametric bootstrap under the no-misconduct model, so flagging thresholds carry explicit false-positive rates.
License:	MIT + file LICENSE
URL:	https://github.com/edidatasolutions/retestR, https://edidatasolutions.github.io/retestR/
BugReports:	https://github.com/edidatasolutions/retestR/issues
Encoding:	UTF-8
Depends:	R (≥ 4.1)
Imports:	stats, utils
Suggests:	knitr, markdown
VignetteBuilder:	knitr
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-09-28 00:33:21 UTC; User
Author:	Daniel Edi [aut, cre, cph]
Maintainer:	Daniel Edi <danieledi2026@gmail.com>
Repository:	CRAN
Date/Publication:	2026-10-08 09:10:07 UTC

Assemble two-attempt repeater data

Description

Assemble two-attempt repeater data

Usage

rt_data(responses, persons, bank)

Arguments

Details

Assumes no item is administered to the same person on both attempts (legitimate item memory would otherwise look like preknowledge).

Value

An 'rt_data' object.

Examples

bank <- data.frame(item = paste0("Q", 1:20), b = rnorm(20),
                   exposed = rep(c(TRUE, FALSE), each = 10))
persons <- data.frame(person = c("A", "B"), days_between = c(60, 200),
                      remediation = c(0, 1))
resp <- data.frame(person = rep(c("A", "B"), each = 20),
                   attempt = rep(rep(1:2, each = 10), 2),
                   item = c(paste0("Q", c(1:5, 11:15, 6:10, 16:20)),
                            paste0("Q", c(6:10, 16:20, 1:5, 11:15))),
                   x = rbinom(40, 1, 0.6))
str(rt_data(resp, persons, bank)$responses)

Evidence statistics for each repeater

Description

Each statistic is a posterior-predictive z-score, positive in the suspicious direction:

'z_gain'

Attempt-2 total score against the distribution predicted from attempt 1 plus the fitted expected growth for this person's covariates. A large gain after long study and remediation is expected; the same gain after two weeks is not.

'z_exposed'

Attempt-2 score on exposed items against the prediction from attempt 1, expected growth and attempt-2 new items. Gains concentrated on exposed items are the signature of preknowledge.

'z_rt'

Mean log-speed on exposed minus new items (lognormal RT model with known time intensities); present when response times are.

Usage

rt_evidence(fit)

Arguments

Value

Data frame: 'person', 'S1', 'S2', 'z_gain', 'z_exposed', and 'z_rt'.

Examples

sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
fit <- rt_fit(sim)
ev <- rt_evidence(fit)
# preknowledge shows up on exposed items and in speed, not only in the gain
aggregate(ev[c("z_gain", "z_exposed", "z_rt")],
          list(preknowledge = sim$truth$preknowledge), mean)

Fit the expected-gain model

Description

Marginal maximum likelihood on a theta grid, in two stages: (1) the attempt-1 ability distribution of repeaters, N(mu1, s1); (2) growth 'theta2 = theta1 + X beta + N(0, sigma_growth)' from attempt-1 responses and attempt-2 responses to **new items only**. Because exposed items never enter the growth model, preknowledge cannot inflate the expected gain, and conditioning on the full attempt-1 likelihood handles regression to the mean.

Usage

rt_fit(
  data,
  growth = ~log(days_between) + remediation,
  grid = seq(-5, 5, by = 0.2)
)

Arguments

Value

An 'rt_fit' object.

Examples

sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
fit <- rt_fit(sim)
fit    # growth coefficients: intercept, log(days_between), remediation

Raw-gain flagging (the common practice, as a baseline)

Description

Raw-gain flagging (the common practice, as a baseline)

Usage

rt_raw_gain(data, threshold = 0.2)

Arguments

Value

Data frame: 'person', 'p1', 'p2', 'gain', 'flag'.

Examples

sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
raw <- rt_raw_gain(sim, threshold = 0.2)
# raw gains also flag honest candidates who remediated
table(flagged = raw$flag, remediation = sim$truth$remediation)

Calibrated risk index for repeat test-takers

Description

Combines evidence statistics into 'T = sum(z)' and calibrates it by parametric bootstrap: 'n_null' complete replicate administrations are simulated under the fitted no-misconduct model (same persons, forms, covariates; attempt-1 ability drawn from each person's posterior; honest growth; honest response times), and the full evidence pipeline is rerun on each. Because the null distribution comes from the same pipeline, the correlation between evidence sources is accounted for, and 'p_value' is an honest false-positive rate for an honest repeater.

Usage

rt_risk(fit, evidence = NULL, n_null = 10, alpha = 0.01, seed = NULL)

Arguments

Value

An 'rt_risk' data frame: evidence columns, 'T', per-component empirical p-values, 'p_value', 'q_value' (Benjamini-Hochberg), 'flag'.

Examples

sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
fit <- rt_fit(sim)
risk <- rt_risk(fit, n_null = 2, alpha = 0.01, seed = 1)
risk
table(flagged = risk$flag, preknowledge = sim$truth$preknowledge)

Simulate repeat test-takers with known misconduct

Description

Honest repeaters grow by 'g0 + g1 * log(days / 30) + g2 * remediation' plus normal noise. A fraction 'p_preknowledge' obtained a random share ('known_frac') of the exposed pool between attempts: on those items they answer correctly with probability 'known_p' and respond 'speedup' log-units faster.

Usage

rt_simulate(
  n_persons = 2000,
  n_exposed_pool = 300,
  n_new_pool = 200,
  form_exposed = 40,
  form_new = 20,
  theta_mean = -0.5,
  theta_sd = 0.7,
  growth = c(0.1, 0.1, 0.4),
  growth_sd = 0.25,
  p_remediation = 0.4,
  p_preknowledge = 0.05,
  known_frac = 0.6,
  known_p = 0.95,
  speedup = 1,
  rt_sd = 0.5,
  seed = NULL
)

Arguments

Value

An 'rt_sim': '$data' (an 'rt_data') and '$truth' (per-person 'theta1', 'theta2', 'growth_mean', 'preknowledge', 'remediation').

Examples

sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
head(sim$truth)
table(sim$truth$preknowledge)