kindling (development version)

kindling 0.2.0

New features

• Added regularization support for neural network models

  • L1 regularization (Lasso) for feature selection via mixture = 1
  • L2 regularization (Ridge) for weight decay via mixture = 0
  • Elastic Net combining L1 and L2 penalties via 0 < mixture < 1
  • Controlled via penalty (regularization strength) and mixture (L1/L2 balance) parameters
  • Follows tidymodels conventions for consistency with glmnet and other packages

• n_hlayers() now fully supports tuning the number of hidden layers

• hidden_neurons() gains support for discrete values via the disc_values argument

  • e.g. disc_values = c(32L, 64L, 128L, 256L)) is now allowed
  • This allows tuning over specific common hidden unit sizes instead of (or in addition to) a continuous range

Implementation fixes

• Tuning methods and grid_depth() is now fixed

  • Parameter space for the number of hidden layers is now fixed and active
  • Corrected parameter space handling for n_hlayers (no more invalid sampling when x > 1)
  • Uses tidyr::expand_grid(), not purrr::cross*()
  • Fix randomization of parameter space which will produce NAs outside from {kindling}‘s own ’dials’
  • No more list columns when n_hlayers = 1

• The supported models now use hardhat::mold(), instead of model.frame() and model.matrix().

Documentation

• Add a vignette to showcase the comparison with other similar packages

• The package description got few clarifications

• Vignette to showcase the comparison with other similar packages

• hidden_neurons parameter now supports discrete values specification

  • Users can specify exact neuron counts via values parameter (e.g., hidden_neurons(values = c(32, 64, 128)))
  • Maintains backward compatibility with range-based parameters (e.g., hidden_neurons(range = c(8L, 512L)) / hidden_neurons(c(8L, 512L)))

• Added \value documentation to kindling-nn-wrappers for CRAN compliance

• Documented argument handling and list-column unwrapping in tidymodels wrapper functions

• Clarified the relationship between grid_depth() and wrapper functions

kindling 0.1.0

• Initial CRAN release
• Higher-level interface for torch package to define, train, and tune neural networks
• Support for feedforward (multi-layer perceptron) and recurrent networks (RNN, LSTM, GRU)
• Integration with tidymodels ecosystem (parsnip, workflows, recipes, tuning)
• Variable importance plots and network visualization tools