Suggests: DBI, RPostgreSQL, stars, knitr, rmarkdown, tinytest

Imports: RANN, units, methods, lwgeom, parallel, s2

Description: 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.
Imports: nabor, units, methods, lwgeom, parallel, s2

person("Luigi", "Ranghetti", email = "luigi.ranghetti@gmail.com", role = c("ctb")),

person("Attilio", "Benini", email = "attilio.benini@gmx", role = c("ctb")),
person("Arnaud", "Tarroux", email = "arnaud.tarroux@nina.no", role = c("ctb")),
Imports: nabor, units, methods, lwgeom, parallel, s2, data.table

person("Attilio", "Benini", email = "attilio.benini@gmx.net", role = c("ctb")),
Imports: nabor, units, methods, parallel, data.table

Title: Non-Negative Lasso and Elastic Net Penalized Generalized Linear
Description: 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.

Author: Baidya Nath Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au>

Author: Vasilis Nikolaidis [aut, cph, cre]
Description: Another collection of neural networks. Includes versions of 'BP', 'Autoencoder', 'LVQ' (supervised and unsupervised), 'MAM'.
Authors@R: person(given = "Vasilis", family = "Nikolaidis", email = "vnikolaidis@us.uop.gr", role = c("aut", "cph", "cre"), comment = c(ORCID = "0000-0003-1471-8788"))

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