HDDM is a python module that implements Hierarchical Bayesian estimation of Drift Diffusion Models.
Introduction
This little convenience package extracts the cython based Navarro and Fuss <https://psycnet.apa.org/record/2009-11068-003>_ likelihoods from the HDDM <https://hddm.readthedocs.io/en/latest/>_ package for fitting hierarchical versions of the Drift Diffusion Model <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2474742>_ .
The code of this repo is essentially taken from the HDDM <https://hddm.readthedocs.io/en/latest/>_ package.
The Copyright information for the original package is:
:Author: Thomas V. Wiecki, Imri Sofer, Mads L. Pedersen, Alexander Fengler, Lakshmi Govindarajan, Krishn Bera, Michael J. Frank :Contact: thomas.wiecki@gmail.com, imri_sofer@brown.edu, madslupe@gmail.com, alexander_fengler@brown.edu, krishn_bera@brown.edu, michael_frank@brown.edu :Web site: https://hddm.readthedocs.io :Github: http://github.com/hddm-devs/hddm :Mailing list: https://groups.google.com/group/hddm-users/ :Copyright: This document has been placed in the public domain. :License: HDDM is released under the BSD 2 license. :Version: 0.9.7
FAQs
HDDM is a python module that implements Hierarchical Bayesian estimation of Drift Diffusion Models.
We found that hddm-wfpt demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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