Metadata-Version: 2.4
Name: gtotr
Version: 0.1.0
Summary: Generalized Tensor-on-Tensor Regression (GToTR)
Author-email: Danny Dunlavy <dmdunla@sandia.gov>, Carlos Llosa <cjllosa@sandia.gov>, Jeremy Myers <jeremy.moulton.myers@gmail.com>
License: BSD 2-Clause License
        
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Project-URL: Homepage, https://github.com/sandialabs/gtotr
Project-URL: Documentation, https://gtotr.readthedocs.io
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Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
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# Generalized Tensor-on-Tensor Regression(GToTR)

`gtotr` is a Python package for generalized tensor-on-tensor regression, 
extending [`statsmodels.GLM`](https://www.statsmodels.org/stable/glm.html) to cases 
with tensor response and tensor covariates. The model parameters in `gtotr` are 
estimated using maximum likelihood estimation associated with a low-rank model of the 
parameter tensor. Currently, `gtotr` provides estimators using low-rank Canonical 
Polyadic (CP) models.

## Getting Started

### Installing 

```bash
$ python -m pip install .
```

Test the install:

```bash
$ python
>>> import gtotr
>>> help(gtotr)
```

## Documentation

- Documentation: [gtotr.readthedocs.io](https://gtotr.readthedocs.io)
- Tutorials: [Jupyter notebook tutorials](tutorials/)
2. Open `gtotr-01-getting-started.ipynb`

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md) for information on participating as a developer.
 
