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arXiv · 2009.09111

FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces

Barinder Thind·Sidi Wu·Richard Groenewald·Jiguo Cao
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Abstract

Neural networks have excelled at regression and classification problems when the input space consists of scalar variables. As a result of this proficiency, several popular packages have been developed that allow users to easily fit these kinds of models. However, the methodology has excluded the use of functional covariates and to date, there exists no software that allows users to build deep learning models with this generalized input space. To the best of our knowledge, the functional neural network (FuncNN) library is the first such package in any programming language; the library has been developed for R and is built on top of the keras architecture. Throughout this paper, several functions are introduced that provide users an avenue to easily build models, generate predictions, and run cross-validations. A summary of the underlying methodology is also presented. The ultimate contribution is a package that provides a set of general modelling and diagnostic tools for data problems in which there exist both functional and scalar covariates.

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Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao. 2020-09-18. FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces. https://arxiv.org/abs/2009.09111

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