Package {SSRTcalc}

Type:	Package
Title:	Monte Carlo and Bayesian Stop-Signal Reaction Time Estimation
Version:	2.1.1
Description:	Estimates stop-signal reaction time (SSRT) in the stop-signal task using the integration and mean methods described by Verbruggen and colleagues (2019) <doi:10.7554/eLife.46323>. In addition to point estimates, the package provides Monte Carlo tools (nonparametric bootstrap confidence intervals, parametric ex-Gaussian simulation, minimum-trial-count and power analysis, and sensitivity analysis under violations of the horse-race assumptions) and Bayesian estimation via 'Stan', including single-subject and hierarchical ex-Gaussian horse-race models with an optional trigger-failure parameter following Matzke and colleagues (2013) <doi:10.1037/a0030543>, posterior inhibition functions, and posterior predictive checks. The Bayesian layer works with either the 'cmdstanr' or 'rstan' backend.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
LazyDataCompression:	xz
RoxygenNote:	7.3.1
Depends:	R (≥ 4.0.0)
Imports:	stats, graphics, grDevices
Suggests:	MASS, cmdstanr (≥ 0.5.0), rstan (≥ 2.26.0), posterior, bayesplot, loo, parallel, testthat (≥ 3.0.0)
Additional_repositories:	https://mc-stan.org/r-packages/
Config/testthat/edition:	3
URL:	https://github.com/agleontyev/SSRTcalc
BugReports:	https://github.com/agleontyev/SSRTcalc/issues
NeedsCompilation:	no
Packaged:	2026-07-05 21:00:57 UTC; agleo
Author:	Anton Leontyev [aut, cre]
Maintainer:	Anton Leontyev <antonleontyev@missouristate.edu>
Repository:	CRAN
Date/Publication:	2026-07-06 06:10:20 UTC

SSRTcalc: Stop-Signal Reaction Time Calculator with Monte Carlo and Bayesian Extensions

Description

Estimates stop-signal reaction time (SSRT) using the integration and mean methods of Verbruggen et al. (2019), and extends these point estimates with three families of tools:

Details

• Monte Carlo (ssrt_boot, ssrt_simulate, ssrt_power, ssrt_robustness): nonparametric bootstrap confidence intervals, parametric ex-Gaussian simulation, minimum-trial-count / power analysis, and sensitivity of SSRT to violations of the horse-race assumptions.

• Bayesian / Stan (ssrt_stan and friends): single-subject and hierarchical ex-Gaussian horse-race models fit via Hamiltonian Monte Carlo, with an optional trigger-failure parameter following Matzke et al. (2013), posterior inhibition functions, and posterior predictive checks. Works with either the cmdstanr or rstan backend.

• run_all_mc and ssrt_stan_compare: convenience wrappers that run a full battery of analyses in one call.

Typical workflow

data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]

integration_adaptiveSSD(d)      # point estimate
ssrt_boot(d)                    # + bootstrap CI
ssrt_stan(d)                    # + full posterior (requires cmdstanr/rstan)

Author(s)

Maintainer: Anton Leontyev anton.leontyev@example.com (ORCID)

References

Verbruggen, F., Aron, A. R., Band, G. P. H., Beste, C., Bissett, P. G., Brockett, A. T., ... Boehler, C. N. (2019). A consensus guide to capturing the ability to inhibit actions and impulses: the stop-signal task. eLife, 8, e46323. doi:10.7554/eLife.46323

Matzke, D., Dolan, C. V., Logan, G. D., Brown, S. D., & Wagenmakers, E.-J. (2013). Bayesian parametric estimation of stop-signal reaction time distributions. Journal of Experimental Psychology: General, 142(4), 1047–1073. doi:10.1037/a0030543

See Also

Useful links:

• https://github.com/agleontyev/SSRTcalc

• Report bugs at https://github.com/agleontyev/SSRTcalc/issues

Example adaptive stop-signal task data

Description

A long-format dataset from a stop-signal task using an adaptive (staircase-tracking) stop-signal delay, suitable for demonstrating every function in SSRTcalc.

Usage

adaptive

Format

A data frame with 4000 rows (20 subjects x 200 trials) and 6 columns:

SubjID

Integer subject identifier (1-20).

trial

Integer trial number within subject (1-200).

vol

Trial type: 0 = go trial, 1 = stop trial.

RT_exp

Observed reaction time in milliseconds. NA on successfully inhibited stop trials.

correct

Accuracy indicator (1 = correct response on go trials, or successful inhibition on stop trials; 0 otherwise).

soa

Stop-signal delay (SOA) in milliseconds for stop trials; NA on go trials. Adjusted trial-by-trial by a staircase algorithm to track ~50% inhibition.

Source

Simulated data distributed with the SSRTcalc package, generated to resemble a typical adaptive stop-signal task following Verbruggen et al. (2019) <doi:10.7554/eLife.46323>.

Examples

data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
integration_adaptiveSSD(d)

Fixed-SSD stop-signal task data (motion discrimination)

Description

A long-format dataset from a stop-signal variant of a random-dot motion-discrimination task in which the stop-signal delay (SOA) was set to one of six fixed values rather than being tracked by a staircase. It complements the adaptive dataset and is intended for demonstrating the fixed-SSD estimators integration_fixedSSD and mean_fixedSSD.

Usage

fixed

Format

A data frame with 28,799 rows (50 subjects) and 8 columns:

SubjID

Integer subject identifier. Subject 2 from the original recording is excluded (see Details), so the IDs run 1 and 3-51.

trial

Trial number within subject (1-576; a subject with one removed trial has 575).

vol

Trial type: 0 = go trial, 1 = stop trial.

RT_exp

Observed reaction time in milliseconds. NA when no response was made (successfully inhibited stop trials and go-trial omissions).

correct

Accuracy indicator (1 = correct go response or successful inhibition; 0 otherwise).

soa

Stop-signal delay (SOA) in milliseconds for stop trials, one of six fixed values (100, 200, 300, 400, 500, 600 ms); NA on go trials, matching the adaptive dataset.

coh

Motion coherence for the go discrimination (0.1, 0.5, or 0.8), an experimental difficulty manipulation.

response

Response direction ("left", "right", or NA when no response was made).

Details

All timing variables are in milliseconds, matching the rest of the package and the adaptive dataset.

During preparation, subject 2 (who had no recorded responses on any trial) and a single stop trial with a recorded reaction time of 0 ms were removed. Subject identifiers otherwise follow the original recording, so identifier 2 is absent.

Source

Experimental data distributed with the SSRTcalc package, from a fixed-SSD motion-discrimination stop-signal task.

Examples

data(fixed)
d <- fixed[fixed$SubjID == 1, ]
integration_fixedSSD(d)
mean_fixedSSD(d)

Estimate SSRT via the integration method (adaptive / staircase SSD design)

Description

Implements the recommended integration method from Verbruggen et al. (2019). For each dataset:

1. Compute p(respond|signal) from all stop trials.

2. Find the nth percentile of the go-RT distribution (n = p_respond).

3. Subtract the mean SSD: SSRT = nth_percentile_RT - mean(SSD).

Usage

integration_adaptiveSSD(
  data,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  min_rt = 50
)

Arguments

Value

A single numeric value: the estimated SSRT in milliseconds.

References

Verbruggen, F., et al. (2019). A consensus guide to capturing the ability to inhibit actions and impulses: the stop-signal task. eLife, 8, e46323. doi:10.7554/eLife.46323

Examples

data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
integration_adaptiveSSD(d)

Estimate SSRT via the integration method (fixed SSD design)

Description

Identical to integration_adaptiveSSD in computation, but intended for experiments using a fixed (constant) stop-signal delay. When multiple fixed SSD values are used, SSRT is computed separately for each SSD and the results are averaged (Verbruggen et al., 2019, Appendix).

Usage

integration_fixedSSD(
  data,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  min_rt = 50
)

Arguments

Value

A single numeric value: the estimated SSRT in milliseconds.

References

Verbruggen, F., et al. (2019). A consensus guide to capturing the ability to inhibit actions and impulses: the stop-signal task. eLife, 8, e46323. doi:10.7554/eLife.46323

Examples

data(fixed)
d <- fixed[fixed$SubjID == 1, ]
integration_fixedSSD(d)

Estimate SSRT via the mean method (adaptive SSD design)

Description

Computes SSRT as the difference between mean go RT and mean SSD:

SSRT = \bar{RT}_{go} - \bar{SSD}

This method is less accurate than the integration method but is included for comparison and historical compatibility.

Usage

mean_adaptiveSSD(
  data,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  min_rt = 50
)

Arguments

Value

A single numeric value: the estimated SSRT in milliseconds.

Examples

data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
mean_adaptiveSSD(d)

Estimate SSRT via the mean method (fixed SSD design)

Description

Computes SSRT as \bar{RT}_{go} - \bar{SSD}. For multiple fixed SSD values the mean is taken across all stop trials.

Usage

mean_fixedSSD(
  data,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  min_rt = 50
)

Arguments

Value

A single numeric value: the estimated SSRT in milliseconds.

Examples

data(fixed)
d <- fixed[fixed$SubjID == 1, ]
mean_fixedSSD(d)

Extract per-group random-effect summaries

Description

A generic function for extracting per-group (e.g. per-subject) parameter summaries from a fitted model. SSRTcalc provides ranef.ssrt_stan for hierarchical Stan fits.

Usage

ranef(object, ...)

Arguments

Value

A method-specific object (typically a data.frame of per-group summaries).

See Also

ranef.ssrt_stan

Extract per-subject random effects from a hierarchical fit

Description

Extract per-subject random effects from a hierarchical fit

Usage

## S3 method for class 'ssrt_stan'
ranef(object, ...)

Arguments

Value

data.frame with per-subject posterior summaries.

Run all four Monte Carlo analyses in one call

Description

Run all four Monte Carlo analyses in one call

Usage

run_all_mc(
  data,
  n_iter = 1000,
  seed = 42,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa"
)

Arguments

Value

Named list: bootstrap, simulation, power, robustness.

Examples

## Not run: 
data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
res <- run_all_mc(d, n_iter=500)

## End(Not run)

Bootstrap confidence intervals for SSRT

Description

Resamples trials with replacement n_iter times and applies the chosen SSRT estimation function to each resample.

Usage

ssrt_boot(
  data,
  ssrt_fn = "integration_adaptiveSSD",
  n_iter = 2000,
  conf = 0.95,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  seed = 42,
  parallel = FALSE,
  n_cores = 2
)

Arguments

Value

Object of class ssrt_boot.

Examples

data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
b <- ssrt_boot(d, n_iter = 500)
print(b)

Minimum trial count analysis via Monte Carlo

Description

Simulates datasets of increasing size and computes SSRT variance as a function of trial count (power curve).

Usage

ssrt_power(
  data,
  trial_counts = c(10, 20, 30, 50, 75, 100, 150, 200),
  n_iter = 500,
  target_se = 10,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  seed = 42
)

Arguments

Value

Object of class ssrt_power.

Examples

data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
p <- ssrt_power(d, trial_counts=c(10,30,50), n_iter=200)
print(p)

Sensitivity of SSRT estimates to horse-race assumption violations

Description

Sweeps three violation types: go/stop process correlation, trigger failure rate, and go-RT shift on stop trials.

Usage

ssrt_robustness(
  data,
  violation = "all",
  n_iter = 500,
  corr_range = seq(0, 0.8, by = 0.1),
  trigger_range = seq(0, 0.4, by = 0.05),
  shift_range = seq(0, 100, by = 10),
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  seed = 42
)

Arguments

Value

Object of class ssrt_robustness.

Examples

data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
r <- ssrt_robustness(d, violation="trigger_failure", n_iter=20,
                     trigger_range=seq(0, 0.2, 0.1))
print(r)

Parametric Monte Carlo SSRT estimation via ex-Gaussian simulation

Description

Fits an ex-Gaussian to observed go-RT data, simulates synthetic datasets under the horse-race model, and estimates SSRT on each.

Usage

ssrt_simulate(
  data,
  n_iter = 2000,
  n_trials = NULL,
  p_stop = NULL,
  ssrt_true = NULL,
  conf = 0.95,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  seed = 42
)

Arguments

Value

Object of class ssrt_simulate.

Examples

data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
s <- ssrt_simulate(d, n_iter=500)
print(s)

Bayesian SSRT estimation via Stan

Description

Fits the independent horse-race model with ex-Gaussian go- and stop-process distributions using Hamiltonian Monte Carlo via Stan. Supports single-subject and multi-subject hierarchical designs, with an optional trigger-failure parameter following Matzke et al. (2013).

Usage

ssrt_stan(
  data,
  model = c("single", "hierarchical"),
  subject_col = NULL,
  trigger_failure = FALSE,
  n_quad = 100,
  chains = 4,
  iter = 2000,
  warmup = 1000,
  cores = 4,
  backend = "auto",
  adapt_delta = 0.95,
  stop_col = "vol",
  rt_col = "RT_exp",
  acc_col = "correct",
  ssd_col = "soa",
  seed = 42,
  ...
)

Arguments

Value

An object of class ssrt_stan.

References

Matzke, D., et al. (2013). Bayesian parametric estimation of stop-signal reaction time distributions. Journal of Experimental Psychology: General, 142(4), 1047–1073.

Examples

## Not run: 
data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]
fit <- ssrt_stan(d, chains = 4, iter = 2000)
print(fit)
plot(fit)

# Hierarchical
fit_h <- ssrt_stan(adaptive, model = "hierarchical",
                   subject_col = "SubjID", chains = 4)
ranef(fit_h)

## End(Not run)

Compare base model vs. trigger-failure model

Description

Fits both models and returns side-by-side SSRT posterior summaries.

Usage

ssrt_stan_compare(data, ...)

Arguments

Value

Invisibly, a list with base, tf, and comparison.

Posterior inhibition function

Description

Computes P(inhibit | SSD) across a range of SSD values using posterior parameter samples, with a 90

Usage

ssrt_stan_inhibition_fn(
  fit,
  ssd_range = seq(0, 600, by = 10),
  n_draws = 400,
  plot = TRUE
)

Arguments

Value

Invisibly, a data.frame with columns: ssd, mean, lo90, hi90.

Leave-one-out cross-validation for an ssrt_stan fit

Description

Requires the Stan model to include a log_lik vector in generated quantities. If absent, an informative error explains how to add it.

Usage

ssrt_stan_loo(fit)

Arguments

Value

A loo object.

Posterior predictive checks for an ssrt_stan fit

Description

Overlays posterior-predictive RT distributions over observed data.

Usage

ssrt_stan_pp_check(fit, n_samples = 200)

Arguments

Value

Invisibly, a list with pp_go_rt and pp_ssrt.

Examples

## Not run: 
data(adaptive)
fit <- ssrt_stan(adaptive[adaptive$SubjID == 1, ])
ssrt_stan_pp_check(fit)

## End(Not run)