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version 0.15
A number of new functions have been implemented and we decided to use a new version numbering system to improve consistency.
version 0.16
Minor bug fix relative to release 0.15. Fixed a transpose operation that would cause normative.py to fail in certain cases
version 0.17:
- New syntax for functions related to parallelization (python path is automatically generated)
- Support for slurm clusters has been added
- Updates to hierarchical Bayesian regression (different priors for intercepts and noise) for improved transfer
- Updated parameterisation for likelihood warping for improved stability
- Now uses a fixed pickle protocol for better interoperability
- Addition of a utility function for loading freesurfer measures
- Updated sphinx documentation
version 0.18
- addition of cross-validation functionality for HBR
- minor bug fixes for HBR
version 0.19
- separate standardization of responses and covariates
- standardization is no longer on by default
- new standardization methods
- pymc3 version fixed
- minor bugs resolved (CV with HBR)
version 0.20
- Major code refactoring
- Requirements updated for python 3.8.3
- Updates to documentation and Integrations with Read the Docs
- Addition of tutorial material
- Performance improvements for BLR (not compatible with previous versions)
- Addition of heteroskedastic noise for BLR
- Addition of Bayesian Information Criterion for BLR
- Addition of utility functions (e.g. design matrix construction)
- Several minor bug fixes (mostly related to HBR and BLR)
version 0.21
Relatively minor updates with respect to version 0.20, including:
- documentation updates
- minor updates to warp (including bug fix for the compostition)
- software version is saved in models
- utility functions for calibration descriptives and extreme values
version 0.22
Some minor updates and bug fixes:
- updates to documentation and tutorials
- updates to normative_parallel (interactive usage)
- updates to HBR (merge functionality)
- other minor bug fixes related to cross-validation (computation of error metrics), import problems and calibration statistics
version 0.23
- SHASH functionality for HBR added
- Bug fix for normative model predict() function
version 0.24
- Minor bug fix related to SHASH/HBR
- Added change log
version 0.25
- Minor bug fixes related to imports
- Minor bug fixes related to SHASH/HBR
- Minor bug fix in normative.py (affecting SMSE)
version 0.26
- Multiple bug fixes, relating to imports, predict() and transfer() functions
- Added support for web portal
- Provided a wrapper for blr to use transfer() functionality
- Also streamlined tutorials (PCNtoolkit-demo), so that all tutorials run with this version
version 0.27
- 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
version 0.28
- 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)
version 0.29
- 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
version 0.30.0
- Minor bug fixes
version 0.31.0
- 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
version 0.32.0
- Update Dockerfile
- Add scaler.transfer, using Welford's algorithm to compute running mean and std for standardizers.
- Correctly save metadata of transfered models.