Implementation of Diffusion decision model with across-trial variability in boundary separation

Diffusion decision model with across-trial variability in boundary separation (sa), drift (sv), and non-decision time (st). This codebase presents the DDM-SA as a fully differentiable PyTensor likelihood for gradient-based Bayesian estimation.

The likelihood is analytic (Navarro–Fuss density; drift variability integrated in closed form, uniform variability by Gauss–Legendre quadrature), so NUTS gets exact gradients. It is validated against the Fortran implementation the model was originally developed in; the Fortran programs, the data, and reference results ship in this repository so every validation is reproducible from a clone.

Install

pip install -e .                # core: numpy, scipy, pytensor
pip install -e ".[sampling]"    # + pymc, arviz, numpyro (pinned jax)
pip install -e ".[hssm]"        # + hssm
pip install -e ".[test]"        # + pytest, numba (reference backend)

Quickstart

from saddm import make_ddmsa_model, sample_ddmsa, sample_ddmsa_exact

data  = sample_ddmsa_exact(a=1.1, z=0.5, v=1.5, t=0.25,
                           sv=0.8, sa=0.5, st=0.08, n_trials=2000)
idata = sample_ddmsa(make_ddmsa_model(data), backend="numpyro")

saddm.ddmsa_logp(rt, response, a, z, v, t, sv, sa, st, sz) is the per-trial log-likelihood; every parameter may be a scalar or a per-trial vector. It has no lapse mixture: HSSM applies its own p_outlier on top of any analytical likelihood, and in plain PyMC one is a pm.logaddexp away.

With HSSM, register saddm.ddmsa_logp as a loglik_kind="analytical" likelihood; examples/estimate_HSSM_saddm.py is the minimal adapter.

Layout

path contents
saddm/ ddmsa.py: the likelihood and PyMC glue. core.py/integrator.py/model.py: the Numba reference implementation (reference extra).
tests/ test_ddmsa.py: verification suite — s = 1 closed forms, agreement with the Numba/Fortran reference, finite-difference gradients, corner finiteness, per-trial broadcasting, backend agreement, static-zero collapse (run directly with --sample for an end-to-end NUTS check). Remaining test_*.py cover the Numba reference.
verification/ parameter_recovery.py: 100-config NUTS recovery study. recovery_figure.py, compare_to_fortran.py, likelihood_figure.py: analysis and figures (read results/reference/ by default; set RESULTS for a fresh run).
examples/ HSSM applications: flat fit on cavanagh_theta, the per-subject + k-sweep replication of the Fortran intertemporal-choice analysis, hierarchical variants, and the random-effects figure.
fortran/ The Fortran programs that produced the benchmarks, with build notes.
data/itc_amasino/ Amasino et al. (2019) trials, the Fortran benchmarks, and the exact k-sweep permutation files.
results/reference/ Reference outputs: recovery, ITC, hierarchical, and cavanagh results with figures. Everything else under results/ is gitignored, and every script writes there by default.

Citation

@unpublished{shevlin2026little,
  author = {Shevlin, Blair R. K. and Fernandez, Kiant{\'e} and Ratcliff, Roger and Krajbich, Ian},
  title  = {A little goes a long way: Fitting one-shot decisions with cognitive models},
  note   = {Manuscript in preparation},
  year   = {2026},
}

Blair R. K. Shevlin* and Kianté Fernandez* contributed equally.

:Author: Kianté Fernandez, Blair R K Shevlin, Roger Ratcliff, Ian Krajbich

:Contact: kiante@ucla.edu, blair.shevlin@mssm.edu, ratcliff.22@osu.edu, krajbich@ucla.edu

Project links

Data verified by PyPI on Sep 3, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for kiante from gravatar.com kiante

Credits

Author: Kianté Fernandez, Blair R K Shevlin, Roger Ratcliff, Ian Krajbich

GitHub Statistics

Data verified by PyPI on Sep 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.11

Provides Extra

sampling hssm reference test

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

saddm-0.1.0.tar.gz (23.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

saddm-0.1.0-py3-none-any.whl (14.3 kB view details)

Uploaded Python 3

File details

Details for the file saddm-0.1.0.tar.gz.

File metadata

  • Download URL: saddm-0.1.0.tar.gz
  • Upload date:
  • Size: 23.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for saddm-0.1.0.tar.gz
Algorithm Hash digest
SHA256 cbf8c8916765aa918d3de3ac26dd1117729050724ac387f256b39e00e10bb858
MD5 5cb6f5f34be1c7bf1def5b180c227f72
BLAKE2b-256 c059179abe8e9810a2593d834b3a40a155b63a4cf2cd00fdd213dd4dcdccf0d1

See more details on using hashes here.

Provenance

The following attestation bundles were made for saddm-0.1.0.tar.gz:

Publisher: publish.yml on kiante-fernandez/saddm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file saddm-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: saddm-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 14.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for saddm-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 12de627cfa3f53bfc19062d25a752abfbdfecf93289f51e26ce6ca8c857c71f8
MD5 cc11a0ca6be45a96e6a939e9a733959b
BLAKE2b-256 6050fc5dfdd62fea06290bf3fe01472018b84a9e9a5b6d77d8e6c07315c0461c

See more details on using hashes here.

Provenance

The following attestation bundles were made for saddm-0.1.0-py3-none-any.whl:

Publisher: publish.yml on kiante-fernandez/saddm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.2.0

2 files

0.1.1

2 files

This release

0.1.0 This release

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page