HDDMhttp://mloss.orgUpdates and additions to HDDMenWed, 24 Apr 2013 02:53:07 -0000HDDM 0.5<html><h1>Purpose</h1> <p>HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making. </p> <h1>Features</h1> <ul> <li> Uses hierarchical Bayesian estimation (via PyMC) of DDM parameters to allow simultaneous estimation of subject and group parameters, where individual subjects are assumed to be drawn from a group distribution. HDDM should thus produce better estimates when less RT values are measured compared to other methods using maximum likelihood for individual subjects (i.e. DMAT or fast-dm). </li> <li> Heavily optimized likelihood functions for speed (Navarro &amp; Fuss, 2009). </li> <li> Flexible creation of complex models tailored to specific hypotheses (e.g. estimation of separate drift-rates for different task conditions; or predicted changes in model parameters as a function of other indicators like brain activity). </li> <li> Estimate trial-by-trial correlations between a brain measure (e.g. fMRI BOLD) and a diffusion model parameter using the HDDMRegression model. </li> <li> Built-in Bayesian hypothesis testing and several convergence and goodness-of-fit diagnostics. </li> </ul></html>thomas wieckiWed, 24 Apr 2013 02:53:07 -0000 estimationdrift modelhierarchical