FuncNN-package: FuncNN: Functional Neural Networks

In FuncNN: Functional Neural Networks

Description

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.

Author(s)

Maintainer: Barinder Thind [e-mail] [copyright holder]

Authors:

• Jiguo Cao

Other contributors:

• Richard Groenewald [contributor]

• Sidi Wu [contributor]

References

Thind, B., Multani, K., and Cao J. (2020) “Deep Learning with Functional Inputs”, https://arxiv.org/abs/2006.09590

See Also

Useful links:

• https://arxiv.org/abs/2006.09590

• https://github.com/b-thi/FuncNN

FuncNN documentation built on Sept. 15, 2020, 5:07 p.m.