- hyperparameter tuning (experimental)
- burnin parameter
- multivariate probit model
- X and Y support (alternative interface)
- negative binomial distribution
- Improved vignette
- Improved README
- dropout is turned off after training (into evaluation mode)
- predict type was changed
- small bug in the activation functions
- extended support for mps devices
- conditional Effects (approximate linear effects)
- uncertainties via bootstrapping (can be forwarded to all functions)
- summary() can return standard errors and p-values for xAI metrics
- improved documentation / several new vignettes
- baseline loss
- loss = inf/na is not captured, training is aborted and user will be warned
- mps (M1/M2 gpu) device is now supported
- early stopping (ignored validation loss)
- weights are only saved for best and last epoch
- gaussian likelihood works now properly
- reguarlization loss is not visualized
- reduce lr on plateau works now with validation loss
- predict function can now return directly the class
- custom loss and parameter can now also be optimized
- summary function (importances) does now support loss = binomial
- print of summary is now more clear
- in ALE function providing new data did not work properly
- Performance improvements with new dataloader
- ALE/PDP work now correctly for softmax
- PDP ICE return now correct curves
- Early stopping works now
- lr reducer on plateau didn't reduce lr
- Predictions are now made on cuda of the model is stored on cuda