TomDLT / tikreg

Tikhonov regression in python

tikreg: Tikhonov regression in Python

tikreg is a Python package that efficiently implements Tikhonov regression.

Tikhonov regression gives us a framework to estimate spatiotemporal encoding models with non-spherical multivariate normal priors. This framework is useful to model biological signals. This package was developed to analyze brain data collected using functional magnetic resonance imaging (fMRI). tikreg can also be used to model other neuroimaging signals (e.g. 2P, ECoG, etc) and LTI signals more generally.

Advantages

• Useful when building large joint models that combine multiple feature spaces
• Transfer function estimation via regularized FIR models
• Efficient implementation of ridge regression for multiple outputs
• Dual and primal solutions for ridge regression

Installation

Clone the repo from GitHub and do the usual python install from the command line

$ git clone https://github.com/gallantlab/tikreg.git
$ cd tikreg
$ sudo python setup.py install

Or with pip:

$ pip install tikreg

Getting started

...Coming soon.

Documentation

https://gallantlab.github.io/tikreg/index.html

Tutorials

moar tutorials coming soon.

Non-spherical MVN prior on features

https://nbviewer.jupyter.org/github/gallantlab/tikreg/blob/master/examples/tutorial_feature_priors.ipynb

Launch Google Colab notebook

Non-spherical MVN prior on temporal delays

https://nbviewer.jupyter.org/github/gallantlab/tikreg/blob/master/examples/tutorial_temporal_priors.ipynb

Launch Google Colab notebook

Banded ridge regression:

https://nbviewer.jupyter.org/github/gallantlab/tikreg/blob/master/examples/tutorial_banded_ridge_polar.ipynb

Cite as

Nunez-Elizalde AO, Huth AG, and Gallant, JL (2019). Voxelwise encoding models with non-spherical multivariate normal priors. NeuroImage. https://doi.org/10.1016/j.neuroimage.2019.04.012