Version 1.1.2

Version 1.1.2

Changes

1. Better default centile plot
2. Improve MSLL computation
3. Speedup model comparison
4. Change default Normal likelihood
5. Save predictions in normal order
6. Switch to SKLearn MAPE
7. Add centered random parameterization
8. Change default offset distribution to ZeroSumNormal

Bug fixes

1. Fix bug with re-submitting a runner's failed jobs
2. Fix bug with incremental file backup function
3. Solve problem that occurs when both n_batches and batch_size are specified
4. Small updates to default prior settings in HBR
5. Improve MSLL computation
6. Fix bug with nm.predict, it is now fully in-place on the Normdata
7. Fix transfer bug for HBR

Version 1.1.1

• Remove preprocessing before evaluation
• Don't recompute centiles in plotter

Version 1.1.0

Version 1.1.0

Major changes

1. Add support for evaluate_logp for transfered BLR models

Bug fixes

1. Fix incorrect application of change-of-variables in BLR evaluate_logp
2. Correctly use semantic versioning
3. Correctly use the __version__ attribute

v1.0.1

Major changes:

1. NormativeModel.merge implemented, completing the federated learning arsenal
2. BLR transfer implemented
3. HBR model comparison using WAIC convenience function in util.model_comparison.py

Other nice things

1. Implemented ARD for BLR
2. Yhat estimation with importance weighting as default method
3. Fixed effect in the slope of mean and variance in BLR
4. Add vertical conditional density plots to plot_centiles function
5. Outlier detection and removal in NormData.from_dataframe
6. Model factory for common regression models

Minor changes:

1. Much faster map_batch_effects
2. Runner loads data results back into NormData objects when parallelise=True and observed=True
3. Remove Torch from dependencies
4. Better support for dataloading from ndarrays
5. Explained Variance in the measures dataframe
6. Avoid annoying Pandas warning in the plotter

v1.0.1 alpha 5

Preserve order of response variables in NormData.merge

v1.0.1 alpha4

HBR model comparison
Much faster Yhat estimation for BLR
BLR transfer

BLR transfer

BLR transfer working

v1.0.1 alpha 2

Improve BLR
Full Changelog: v1.0.1-alpha...v1.0.1-alpha2

v1.0.1-alpha: Improve BLR

Some important changes, mostly affecting BLR

• remove numpy version pin
• add linear component to bspline basis function
• change default parameters for n_iter and tolerance in BLR
• move computation of lambda n_vec to after reparameterization of beta
• add skeleton for fixed_effect_slope

PCNtoolkit version 1.0.0

Major code refactor (effectively a re-write of the toolbox). Key changes include:

• Improved object-orientation
• Improved code readability
• Improved in-line documentation
• Human readable models (JSON format)
• Improved extendability
• Algorithmic improvements, e.g. merge, extend, transfer, harmonise, synthesize and improvments to HBR
• Completely revised documentation
• No longer supports Gaussian process regression
• Updated cluster job submission system

IMPORTANT:
Models estimated on earlier versions of PCNtoolkit (up until v0.39) are NOT COMPATIBLE with this release. Please install the old version to continue working with legacy models