Training of neural networks using backpropagation,
    resilient backpropagation with (Riedmiller, 1994) or without
    weight backtracking (Riedmiller and Braun, 1993) or the
    modified globally convergent version by Anastasiadis et al.
    (2005). The package allows flexible settings through
    custom-choice of error and activation function. Furthermore,
    the calculation of generalized weights (Intrator O & Intrator
    N, 1993) is implemented.

Visualization and analysis tools to aid in the interpretation of
    neural network models.  Functions are available for plotting,
    quantifying variable importance, conducting a sensitivity analysis, and
    obtaining a simple list of model weights.

A small multilayer perceptron implementation for
    'R'. It supports regression and classification, multiple hidden layers,
    mini-batch training, Adam, SGD, momentum, Nesterov, RPROP, GRPROP and
    L-BFGS optimizers, dropout, L2 regularization, early stopping, convergence
    thresholds, gradient clipping, sample and class weights, callback hooks,
    target scaling and robust Huber loss for regression, 'Rcpp' forward-pass
    kernels, formula interfaces, model evaluation with balanced classification
    metrics, cross-validation, compact tuning, permutation importance, model
    persistence helpers, and 'S3' prediction methods. Methods follow
    Rumelhart, Hinton and Williams (1986) <doi:10.1038/323533a0>, with
    optimizers including Riedmiller and Braun (1993)
    <doi:10.1109/ICNN.1993.298623>, Nocedal (1980)
    <doi:10.1090/S0025-5718-1980-0572855-7>, and Kingma and Ba (2014)
    <doi:10.48550/arXiv.1412.6980>.

A native R implementation of neural simulation-based inference, focused on Neural Posterior Estimation. Given a prior over parameters and a simulator, 'neuralsbi' trains a conditional neural density estimator to approximate the Bayesian posterior, enabling amortized, likelihood-free inference. Neural estimators run on the 'torch' back end. It targets applied researchers who want an approachable interface with sensible defaults and built-in posterior diagnostics.