Pcntoolkit

- Update Dockerfile
- Add scaler.transfer, using Welford's algorithm to compute running mean and std for standardizers.
- Correctly save metadata of transfered models.

- Major changes:
- Move to Poetry for dependency management in pyproject.toml.
- PCNToolkit must now be installed using python -m pip install .. See the README for complete instructions.
- A CLI command normative is automatically created, and can be used instead of python normative.py.
- Nutpie can be used as a sampler for HBR by setting `nuts_sampler='nutpie''. Nutpie and numba must first be installed using conda.
- Minor changes
- torque jobs now support multicore jobs via the keyword 'n_cores_per_batch'
- Backwards compatibilty improved by using pd.read_pickle instead of pickle.load
- SHASH classes have been refactored and improved
- HBR priors improved

- Minor bug fixes

- Bug fixes (e.g. HBR predict crash, normative_paralell support for .txt input)
- Added docstrings for most functions
- Fixed some problems with exception handling
- Formatted whole project with autopep8
- Addedd functionality to compute SHASH z-scores from normative.py
- Updated requirements
- Basic pytest continuous integration framework implemented

- Updated to PyMC5 (including migrating back-end to PyTensor)
- Added support for longitudinal normative modelling with BLR (see Buckova-Rehak et al 2023)
- Changed default optimiser for trend surface models (for scalability)

- Configured more sensible default options for HBR (random slope and intercept for mu and random intercept for sigma)
- Fixed a translation problem between the previous naming convention for HBR models (only Gaussian models) and the current naming (also SHASH models)
- Minor updates to fix synchronisation problems in PCNportal (related to the HBR updates above)
- Added configuration files for containerisation with Docker