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.
- 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
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
APPOLOGIES FOR THE LACK OF DOCUMENTATION!
Working on it! In the mean time, please refer to the unitests.
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