FuncNN: Functional Neural Networks

A collection of functions which fit functional neural network models. In other words, this package will allow users to build deep learning models that have either functional or scalar responses paired with functional and scalar covariates. We implement the theoretical discussion found in Thind, Multani and Cao (2020) <arXiv:2006.09590> through the help of a main fitting and prediction function as well as a number of helper functions to assist with cross-validation, tuning, and the display of estimated functional weights.

Version: 1.0
Imports: keras, tensorflow, fda.usc, fda, ggplot2, ggpubr, caret, pbapply, reshape2, flux, doParallel, foreach, Matrix
Suggests: knitr, rmarkdown
Published: 2020-09-15
Author: Richard Groenewald [ctb], Barinder Thind [aut, cre, cph], Jiguo Cao [aut], Sidi Wu [ctb]
Maintainer: Barinder Thind <barinder.thi at gmail.com>
License: GPL-3
URL: https://arxiv.org/abs/2006.09590, https://github.com/b-thi/FuncNN
NeedsCompilation: no
Citation: FuncNN citation info
Materials: README
CRAN checks: FuncNN results

Documentation:

Reference manual: FuncNN.pdf

Downloads:

Package source: FuncNN_1.0.tar.gz
Windows binaries: r-devel: FuncNN_1.0.zip, r-release: FuncNN_1.0.zip, r-oldrel: FuncNN_1.0.zip
macOS binaries: r-release (arm64): FuncNN_1.0.tgz, r-release (x86_64): FuncNN_1.0.tgz, r-oldrel: FuncNN_1.0.tgz

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