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) <doi:10.48550/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 |
| DOI: |
10.32614/CRAN.package.FuncNN |
| 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:
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