Fast Compact Multilayer Perceptrons // 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.