Software for feed-forward neural networks with a single
  hidden layer, and for multinomial log-linear models.

An implementation of a Neural Network using the
    Levenberg-Marquardt optimization from 'minpack.lm', ideal for small
    datasets. For more details see Moré (1978) <doi:10.1007/BFb0067700>.

Automatic time series modelling with neural networks. 
    Allows fully automatic, semi-manual or fully manual specification of networks. For details of the
	specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>;
	and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.

Functions to compute the non-negative garrote estimator as 
             proposed by Breiman (1995) <https://www.jstor.org/stable/1269730>
             with the penalized initial estimators extension as proposed by 
             Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.

K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor search uses (1) C code from 'GeographicLib' for lon-lat point layers, (2) function knn() from package 'nabor' for projected point layers, or (3) function st_distance() from package 'sf' for line or polygon layers. The package also includes several other utility functions for spatial analysis.

Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.

Contains a module to define neural networks from custom components and versions of Autoencoder, BP, LVQ, MAM NN.

An R interface to the Lawson-Hanson implementation of an
        algorithm for non-negative least squares (NNLS).  Also allows
        the combination of non-negative and non-positive constraints.