nnlib2Rcpp

An R package providing access to Neural Networks created using nnlib2.

A tool for creating custom neural networks in C++ and using them in R. The Artificial Neural Networks (ANN), also called Artificial Neural Systems (ANS) or simply Neural Networks (NN) are created using the 'nnlib2' C++ library.

Currently also includes predefined versions of BP, Autoencoder, MAM, LVQ (supervised and unsupervised).

All NNs are created using 'nnlib2' (a C++ library of classes for implementing NNs) and interfaced with R via RCpp.

The package also provides the NN R module (Class "NN") which allows creation and control of custom NNs configurations and topologies from R. These NNs consist of components (predefined or user-defined) usually defined via 'nnlib2'. To add new user-defined NN components (layers, nodes, connections, sets of connections etc) to the package see the "NN" component documentation (type ?NN in R). Note: defining components via 'nnlib2' requires some familiarity with C++.

To install:

(a) From CRAN Repository (recommended): The CRAN (stable) version of this package can be installed the usual way, i.e. by invoking the following R command:

install.packages("nnlib2Rcpp") 

(b) From GitHub: To add the GitHub (latest) version of this package to your R installation, use the following R commands:

library(devtools) 
install_github("VNNikolaidis/nnlib2Rcpp")

(c) From r-universe: To add the package (corresponding to the latest GitHub release version) to your R installation, use the following R command:

install.packages('nnlib2Rcpp', repos = 'https://vnnikolaidis.r-universe.dev')

Once installed, for package help (including documentation and examples for each function or class provided by nnlib2Rcpp) use the following R command:

help(package='nnlib2Rcpp')

while the package vignette (containing information on adding custom components) can be viewed using the following R command:

vignette("intro", package='nnlib2Rcpp')

The package vignette is also available in PDF format here:

https://github.com/VNNikolaidis/nnlib2Rcpp/blob/master/support/manual.pdf

A reference manual in PDF format (for the last version in CRAN) can be found here:

https://cran.r-project.org/web/packages/nnlib2Rcpp/nnlib2Rcpp.pdf

For information on citing this package use the following R command:

citation("nnlib2Rcpp")

For copyright information see LICENSE.md file or DESCRIPTION+LICENSE files (as imposed by package format for CRAN submissions).

The ‘nnlib2’ library used (and included) in this package is a collection of C++ base classes and templates for creating NNs. This library is also available as a standalone project, in GitHub repository (https://github.com/VNNikolaidis/nnlib2). For a (simplified) class-diagram of significant nnlib2 classes and templates see: https://github.com/VNNikolaidis/nnlib2/blob/master/misc/diagram%20of%20main%20classes.png

For implementing new NN components and models in nnlib2 that can be used in nnlib2Rcpp, see also:

https://r-posts.com/creating-custom-neural-networks-with-nnlib2rcpp/ ( permalink: https://wp.me/p8rgs6-sh )

As mentioned earlier, more instructions on using 'nnlib2' and 'nnlib2Rcpp' can be found in the package vingette, also available in PDF format here:

https://github.com/VNNikolaidis/nnlib2Rcpp/blob/master/support/manual.pdf

Link to related paper in the Journal of Open Source Software:

Known future goals for this project (iF AND WHEN time permits) include:

• adding more classic neural network component and model implementations in ‘nnlib2’ (and thus 'nnlib2Rcpp').
• adding parallelism to ‘nnlib2’ component base classes.
• adding support for package 'nn2poly' (https://arxiv.org/pdf/2406.01588)

Let us know if interested to contribute, or want to add your neural network components to the package. Or, as stated below:

We invite anyone to contribute to this software and/or provide feedback, suggestions, report issues or problems. Possible improvements and contributions may include (but are not limited to) implementation of additional neural network models using nnlib2 classes and templates (and thus new neural network components compatible with "NN" module in nnlib2Rcpp), parallelism (possibly via OpenMP), replacement of custom data structures with STL containers, performance enhancements etc.

Please use the issues option in GitHub or email (vnnikolaidis AT gmail.com) if interested to contribute.