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

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.

Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6), 1336-1353 <doi:10.1109/TKDE.2012.51> and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2), 713-730 <doi:10.1137/07069239X>.