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neuralGAM 2.0.1
- Minor bug fix related to the exportation of S3 methods (autoplot,
predict).
- Minor corrections in documentation
neuralGAM 2.0.0
- Major update with expanded flexibility, improved
diagnosis tools, and uncertainty quantification.
- Additional distribution families: now supports
poisson in addition to gaussian and
binomial.
- Per-term architecture configuration:
hyperparameters (units, activation, learning rate, initializers,
regularizers) can now be set per smooth term inside
s().
- Confidence Intervals (CI):
uncertainty_method argument allows estimation of
epistemic uncertainty.
- Intervals integrated into
predict() and
autoplot().
- Cross-validation support: new
validation_split parameter for monitoring validation losses
during training.
- Training diagnostics: new
plot_history() function for visualizing training/validation
loss curves per term and per backfitting iteration.
- Improved summary(): displays per-term
configuration, layer architectures, linear coefficients, and compact
training history.
- Diagnosis plots: new
diagnose()
function which provides a 2×2 diagnostic panel.
- Autoplot enhancements: ggplot2-based diagnostic and
effect plots with support for CI ribbons, per-term inspection, and
factor vs continuous term visualization.
- Testing: expanded test coverage for new families,
CI estimation, plotting, and per-term configuration.
- Internal refactoring:
- Clean separation of deviance and link functions per family.
- Consistent handling of sample weights.
- Improved numerical stability (clamping in
log/exp/probabilities).
neuralGAM 1.1.1
verbose parameter is now used along all the required
functions.
Tensorflow and Keras are now loaded when
library(neuralGAM) is invoked for the first time, and
therefore the first run of the neuralGAM() function has all
the required packages ready.
neuralGAM 1.1.0
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.