Poplar

Latest version: v0.2

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0.2

poplar` is a lightweight package for performing selection bias modelling with machine learning. It is useful when the selection process can only be modelled at a high computational cost, and is efficient and accurate even at high dimensionality. It is fully implemented with `pytorch`.

If you find `poplar` useful in your work, please cite both [Chapman-Bird et al. (2023)](https://doi.org/10.1093/mnras/stad1397) and the [package doi](https://doi.org/10.5281/zenodo.7573034).

0.2.0

- Fixed a bug that prevented loading of a saved `LinearModel` on a machine with no GPU available in the slot that the model was originally saved on. Now, models are always moved to CPU prior to pickling.
- Fixed a bug when using the `IdentityRescaler` that prevented moving of models to GPU
- Some documentation changes and other minor bug fixes.

0.1.0

The first release of poplar to accompany the paper submission.

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