Package: AccumulatR 1.0.0

AccumulatR: Simulation and Likelihood Tools for Evidence-Accumulation Models

Toolbox for designing, simulating and fitting custom racing evidence-accumulation models to behavioral data. Users define a model in terms of accumulators, pooled channels, logical response rules, and timing dependencies. This allows more direct embedding of psychological hypotheses into the model. With that model you can simulate decision-making data and evaluate the likelihood of observed responses under alternative parameter values. The user-facing interface stays in R, while the simulation and likelihood calculations are handled in C++ for speed.

Authors:Niek Stevenson [aut, cre]

AccumulatR_1.0.0.tar.gz
AccumulatR_1.0.0.zip(r-4.7-x86_64)AccumulatR_1.0.0.zip(r-4.7-arm64)AccumulatR_1.0.0.zip(r-4.6-x86_64)AccumulatR_1.0.0.zip(r-4.6-arm64)AccumulatR_1.0.0.zip(r-4.5-x86_64)
AccumulatR_1.0.0.tgz(r-4.6-x86_64)AccumulatR_1.0.0.tgz(r-4.6-arm64)AccumulatR_1.0.0.tgz(r-4.5-x86_64)AccumulatR_1.0.0.tgz(r-4.5-arm64)
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AccumulatR_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
AccumulatR/json (API)

# Install 'AccumulatR' in R:
install.packages('AccumulatR', repos = c('https://niekstevenson.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/niekstevenson/accumulatr/issues

Pkgdown/docs site:https://niekstevenson.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

6.61 score 3 stars 1 scripts 25 exports 2 dependencies

Last updated from:9646446604. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK139
linux-devel-x86_64OK184
source / vignettesOK426
linux-release-arm64OK184
linux-release-x86_64OK144
macos-release-arm64OK235
macos-release-x86_64OK416
macos-oldrel-arm64OK231
macos-oldrel-x86_64OK436
windows-devel-arm64OK409
windows-devel-x86_64OK215
windows-release-arm64OK408
windows-release-x86_64OK206
windows-oldrel-x86_64OK167
wasm-releaseOK136

Exports:add_accumulatoradd_componentadd_outcomeadd_pooladd_triggerafterall_ofbuild_outcome_exprbuild_param_matrixcomplexity_metricsfinalize_modelfirst_ofinhibitlog_likelihoodmake_contextnone_ofpar_namesplot_accumulatorsprepare_dataprocessing_treerace_specresponse_probabilitiesset_mixtureset_parameterssimulate

Dependencies:BHRcpp

A Simple Race Model
Define the model | Simulate data | Evaluate the likelihood | Estimate parameters with optim()
Rendered fromsimple_model.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-04

Chained Onsets
Define the model | Simulate data | Estimate parameters with optim()
Rendered fromchained_onset.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-04

Getting Started with AccumulatR
The basic workflow | Core ideas | trials | R | rt | Component | Onset | A minimal model | Simulated behavioral data | From behavioral data to likelihood
Rendered fromgetting_started.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-03

Logical Rules
What a response rule does | The four main helpers | first_of(): either route can generate the response | all_of(): several things must all be true | none_of(): the response requires the competing event to be absent | inhibit(): the response is driven by one process and blocked by another | none_of() versus inhibit() | A combined example
Rendered fromlogical_rules.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-06

Multiple Outcomes
Define the model | Simulate data | Estimate parameters with optim()
Rendered frommulti_outcome.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-03

Pooling Accumulators
Define the model | Simulate data | Estimate parameters with optim()
Rendered frompool_model.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-13

Shared Absence Triggers
Define the model | Simulate data | Evaluate the likelihood | Estimate parameters with optim()
Rendered fromtrigger_model.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-13

Supported Distributions
Naming convention | Available distributions | Example
Rendered fromdistributions.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-13

Working with Mixtures
A common architecture | Fixed mixture weights with observed components | Sampled mixture weights with latent components | Estimate a sampled-mixture weight
Rendered frommixtures.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-04-13