  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.
