Equine

What's Changed
*Enable concomitant visualization capability for the GP in the web-app by nukularrr in https://github.com/mit-ll-responsible-ai/equine/pull/36
*Removed no_grad statement from GP forward method and moved model to device in gp by gtbotkin in https://github.com/mit-ll-responsible-ai/equine/pull/42
*Various dependency updates in github actions, hypothesis, etc.

New Contributors
* gtbotkin made their first contribution in https://github.com/mit-ll-responsible-ai/equine/pull/42

**Full Changelog**: https://github.com/mit-ll-responsible-ai/equine/compare/v0.1.2...v0.1.3

What's Changed
* Enable concomitant visualization capability for the GP in the web-app by nukularrr in https://github.com/mit-ll-responsible-ai/equine/pull/36
* Removed no_grad statement from GP forward method and moved model to device in gp by gtbotkin in https://github.com/mit-ll-responsible-ai/equine/pull/42
* Various dependency updates in github actions, hypothesis, etc.

New Contributors
* gtbotkin made their first contribution in https://github.com/mit-ll-responsible-ai/equine/pull/42

**Full Changelog**: https://github.com/mit-ll-responsible-ai/equine/compare/v0.1.2...v0.1.3rc

The goal of this package is to make it simple to add modern uncertainty quantification (UQ) techniques to existing PyTorch models to produce label predictions with calibrated probabilities and out-of-distribution indicators.

What's Changed
Overall: two minor bugfixes, testing coverage increased, improved type hinting, and more comprehensive CI tools and actions.

* Feature/beartype by nukularrr in https://github.com/mit-ll-responsible-ai/equine/pull/21
* Stevenjson/patch 1 by nukularrr in https://github.com/mit-ll-responsible-ai/equine/pull/22
* Distance update by RoundOffError in https://github.com/mit-ll-responsible-ai/equine/pull/28
* Auto-updated dependencies with dependabot

**Full Changelog**: https://github.com/mit-ll-responsible-ai/equine/compare/v0.1.1...v0.1.2

EQUI(NE)^2 (equine): Establishing Quantified Uncertainty for Neural Networks

The goal of this package is to make it simple to add modern uncertainty quantification (UQ) techniques to existing PyTorch models to produce label predictions with calibrated probabilities and out-of-distribution indicators.

EQUI(NE)^2 (equine): Establishing Quantified Uncertainty for Neural Networks

The goal of this package is to make it simple to add modern uncertainty quantification (UQ) techniques to existing PyTorch models to produce label predictions with calibrated probabilities and out-of-distribution indicators.

What's Changed
* Added the Zenodo-linked DOI via GitHub integration.
* Added automatic versioning via `setuptools_scm`

Full Changelog: https://github.com/mit-ll-responsible-ai/equine/compare/v0.1.1rc4...v0.1.1rc5

Minor changes to add Zenodo DOI