Viprs

Changed

- Fixed bugs in the E-Step benchmarking script.
- Re-wrote the logic for finding BLAS libraries in the `setup.py` script. :crossed_fingers:
- Fixed bugs in CI / GitHub Actions scripts.

Added

- `Dockerfile`s for both `cli` and `jupyter` modes.

A large scale restructuring of the code base to improve efficiency and usability.

Changed

- Moved plotting script to its own separate module.
- Updated some method names / commandline flags to be consistent throughout.
- Updated the `VIPRS` class to allow for more flexibility in the optimization process.
- Removed the `VIPRSAlpha` model for now. This will be re-implemented in the future,
using better interfaces / data structures.
- Moved all hyperparameter search classes/models to their own directory.
- Restructured the `viprs_fit` commandline script to make the code cleaner,
do better sanity checking, and introduce process parallelism over chromosomes.

Added

- Basic integration testing with `pytest` and GitHub workflows.
- Documentation for the entire package using `mkdocs`.
- Integration testing / automating building with GitHub workflows.
- New self-contained implementation of E-Step in `Cython` and `C++`.
- Uses `OpenMP` for parallelism across chunks of variants.
- Allows for de-quantization on the fly of the LD matrix.
- Uses BLAS linear algebra operations where possible.
- Allows model fitting with only
- Benchmarking scripts (`benchmark_e_step.py`) to compare computational performance of different implementations.
- Added functionality to allow the user to track time / memory utilization in `viprs_fit`.
- Added `OptimizeResult` class to keep track of the info/parameters of EM optimization.
- New evaluation metrics
- `pseudo_metrics` has been moved to its own module to allow for more flexibility in evaluation.
- New evaluation metrics for binary traits: `nagelkerke_r2`, `mcfadden_r2`,
`cox_snell_r2` `liability_r2`, `liability_probit_r2`, `liability_logit_r2`.
- New function to compute standard errors / test statistics for all R-Squared metrics.

Changed

- Removed the `--fast-math` compiler flag due to concerns about
numerical precision (e.g. [Beware of fast-math](https://simonbyrne.github.io/notes/fastmath/)).

Added

- New implementation for the e-step in `VIPRS`, where we multiply with the rows of the
LD matrix only once.
- Added support for deterministic annealing in the `VIPRS` optimization.
- Added support for `pseudo_validation` as a metric for choosing models. Now, the
`VIPRS` class has a method called `pseudo_validate`.
- New implementations for grid-based models: `VIPRSGrid`, `VIPRSGridSearch`, `VIPRSBMA`.
- New python implementation of the `LDPredinf` model, using the `viprs`/`magenpy`
data structures.
- MIT license for the software.

Changed

- Corrected implementation of Mean Squared Error (MSE) metric.
- Changed the `c_utils.pyx` script to be `math_utils.pyx`.
- Updated documentation in `README` to follow latest APIs.

Changed

- Updating the dependency structure between `viprs` and `magenpy`.

Added

- Refactoring the code in the `viprs` repository and re-organizing it into a python package.
- Added a module to compute predictive performance metrics.
- Added commandline scripts to allow users to access some of the functionalities of `viprs` without
necessarily having to write python code.
- Added the estimate of the posterior variance to the output from the module.

Changed

- Updated plotting script.
- Updated implementation of `VIPRSMix`, `VIPRSAalpha`, etc. to inherit most
of their functionalities from the base `VIPRS` class.
- Cleaned up implementation of hyperparameter search modules.