Changes in version 0.3.0.9000                      

New features

  - autoplot() and plot() methods for nn_fit objects now visualize the
    training loss history, with optional validation loss and an
    early-stopping marker when early stopping fires.

  - autoplot_diagnostics() and plot_diagnostics() produce prediction
    diagnostic plots for nn_fit objects: residuals vs fitted and actual
    vs fitted panels for regression, one panel per output for
    multi-output regression, and a confusion matrix heatmap for
    classification.

Ongoing features

  - More visualization supports
    
      - NN architecture plot

                 Changes in version 0.3.0 (2026-03-03)                  

New Experimental functions

  - Generalized nn_module() expression generator to generate
    torch::nn_module() expression for the same sequential NN
    architectures
    
      - This is how you use to generate nn_module() for 1D-CNN
        (Convolutional Neural Networks) with 3 hidden layers:
    
    nn_module_generator(
        nn_name = "CNN1DClassifier",
        nn_layer = "nn_conv1d",
        layer_arg_fn = ~ if (.is_output) {
            list(.in, .out)
        } else {
            list(
                in_channels = .in,
                out_channels = .out,
                kernel_size = 3L,
                stride = 1L,
                padding = 1L 
            )
        },
        after_output_transform = ~ .$mean(dim = 2),
        last_layer_args = list(kernel_size = 1, stride = 2),
        hd_neurons = c(16, 32, 64),
        no_x = 1,
        no_y = 10,
        activations = "relu"
    )

  - train_nn() to execute nn_module_generator()
    
      - nn_arch() must be supplied to inherit extra arguments from
        nn_module_generator() function.
      - Allows early stopping if early_stopping is supplied with
        early_stop().
      - Supported with several data types: matrix, data.frame, dataset
        ({torch} dataset), and a formula interface.
      - train_nnsnip() is now provided to bridge train_nn() with
        {tidymodels}

  - You can supply customized activation function under act_funs() with
    new_act_fn().
    
      - Activation functions that especially don't exist on
        torch::nnf_*().
      - Supply the argument with a function
      - The function supplied into new_act_fn() must return a torch
        tensor object.
      - Example: act_funs(new_act_fn(torch::torch_tanh)) or
        act_funs(new_act_fn(\(x) torch::torch_tanh(x)))
      - Use .name as a displayed name of the custom activation function.

Superset

  - act_funs() as a DSL function now supports index-style parameter
    specification for parametric activation functions
    
      - Activation functions can now be modified using [ syntax (e.g.
        softplus[beta = 0.2])
      - The current args() (e.g. softplus = args(beta = 0.2)) is now
        superseded by that.

Bug Fixes

  - No suffix generated for 13 by ordinal_gen(). Now fixed.

  - hd_neurons for both ffnn_generator() and rnn_generator() accepts
    empty arguments, which implies there's no hidden layers applied.

                 Changes in version 0.2.0 (2026-02-04)                  

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 has been clarified

  - 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

                 Changes in version 0.1.0 (2026-01-31)                  

  - 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