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FuncNN  

Functional Neural Networks
View on CRAN: Click here


Download and install FuncNN package within the R console
Install from CRAN:
install.packages("FuncNN")

Install from Github:
library("remotes")
install_github("cran/FuncNN")

Install by package version:
library("remotes")
install_version("FuncNN", "1.0")



Attach the package and use:
library("FuncNN")
Maintained by
Barinder Thind
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-09-15
Latest Update: 2020-09-15
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) 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.
How to cite:
Barinder Thind (2020). FuncNN: Functional Neural Networks. R package version 1.0, https://cran.r-project.org/web/packages/FuncNN. Accessed 08 Mar. 2026.
Previous versions and publish date:
No previous versions
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Complete documentation for FuncNN
Functions, R codes and Examples using the FuncNN R package
Some associated functions: FuncNN-package . daily . fnn.cv . fnn.fit . fnn.fnc . fnn.plot . fnn.predict . fnn.tune . tecator . 
Some associated R codes: PackageFuncNN.R . daily.R . fnn.cv.R . fnn.fit.R . fnn.fnc.R . fnn.plot.R . fnn.predict.R . fnn.tune.R . tecator.R .  Full FuncNN package functions and examples
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