This project is still under development. This project contains a selection of models and data tools I have created, modified, and used for different projects.
- Selectivity Model: model that non-linearly integrates input features to produce concave and convex isoresponse surfaces
- Partial Least Squares: modification of sklearn.pls models to allow for optional bias removal and orthogonalization of rotations and loadings
- RbfModel: modified and sklearn-compatible version of the scipy.interpolate.RBFInterpolator model
- Collection of probabilistic circuit model designs using pytorch and pyro
- These models allow you to model circuits and incorporating anatomical constraints as priors
- Deep implicit circuit models designed using pytorch and lightning
- The models allow you to model constrained circuits assuming the observed responses are at steady-state
- Rank1PlusSparse: linear model with a rank one constraint and an added sparse weight matrix
- RankConstraint: linear model with a rank constraint (different approach to PLS)
- Tikhonov regression
- TwoLayerEncodingModel: two layer encoding model with different objective functions
This package was created with the help of the scikit-learn templating tool: https://github.com/scikit-learn-contrib/project-template