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

Contents:

ceode: Robust estimation of convolution based DCMs for evoked responses
HGF: The Hierarchical Gaussian Filter; Bayesian inference on computational processes from observed behaviour
HUGE: Variational Bayesian inversion for hierarchical unsupervised generative embedding
MICP: Bayesian Mixed-effects Inference for Classification Studies
MPDCM: Efficient integration of DCMs using massive parallelization
PhysIO: Physiological Noise Correction for fMRI
rDCM: DCM based, efficient inference on effective brain connectivity for fMRI
SEM: SERIA Model for Eye Movements (saccades & anti-saccades) and Reaction Times
VBLM: Variational Bayesian Linear Regression
FDT: Filter Detection Task

TAPAS is written in MATLAB & distributed under GNU GPL V3.