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LBBNN 0.1.1 (development version)

• Initial tests added; cleanup for R CMD check portability.
• Initial CRAN submission.

LBBNN 0.1.2 (minor bug fix)

• Fix bug in LBBNN_Linear so layers work properly in convolutional architecture.
• Updated experiment on convolutional architecture for R journal submission.
• Added default values to mpm and draws in predict() function.

LBBNN 0.1.3 (minor cosmetic changes)

• Changed some function names to be consistent with CRAN guidelines.
• Removed export of function that is only used internally.
• Changed assignment operator to be consistent everywhere.
• Removed datasets not used in RJ article submission.

LBBNN 0.1.4 (minor bug fix)

• Added relu to input-skip layers. Updated experiments.

LBBNN 0.1.5

• All torch-dependent examples are now guarded by torch_available() to ensure safe execution when torch or libtorch is not available.

LBBNN 0.1.6 (resubmission to R journal)

• Added more vignettes, built pkgdown website.
• More robust testing.
• Bug fix in conv2d layer.
• Small changes to make training on gpu more efficient.
• Added possibility to turn off printing to console during training.
• Utility functions work with standard LBBNN architecture without input-skip.
• predict() and validate_lbbnn() no longer change the model object.
• fixed bug that compute_paths() was not being called for LBBNN models.
• added initialization keywords such as ‘dense’ or ‘balanced’ for inclusion probabilities.
• added possibility for different weight initializations.

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.