TAPAS - Translational Algorithms for Psychiatry-Advancing Science

TAPAS is a collection of algorithms and software tools developed by the Translational Neuromodeling Unit (TNU, Zurich) and collaborators. The goal of these tools is to support clinical neuromodeling, particularly computational psychiatry, computational neurology, and computational psychosomatics.

Currently, TAPAS includes the following packages:

HGF: Hierarchical Gaussian Filter; Bayesian inference on computational processes from observed behavior

HUGE: Variational Bayesian inversion for hierarchical unsupervised generative embedding

MICP: Bayesian Mixed-effects Inference for Classification Studies

MPDCM: Massively Parallel DCM; Efficient numerical integration

PhysIO: Physiological Noise Correction for fMRI

rDCM: Efficient inference on effective brain connectivity for fMRI.

SEM: SERIA Model for (anti-)Saccadic Eye Movements and Reaction Times

VBLM: Variational Bayesian Linear Regression

TAPAS is written in MATLAB and distributed as open source code under GNU General Public License 3.0.

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Specifications

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Other Keywords:
Bayesian, DCM, fMRI, HGF, PhysIO, SPM

Associations

has subpart:
can be extended by:
SPM
works well with:
SPM