For anyone interested, nnlib2Rcpp is an R package containing a number of Neural Network implementations and is available on GitHub. It can be installed as follows (the usual way for packages on GitHub):
The NNs are implemented in C++ (using nnlib2 C++ class library) and are interfaced with R via Rcpp package (which is also required).
The package currently includes the following NN implementations:
- A Back-Propagation (BP) multi-layer NN (supervised) for input-output mappings.
- An Autoencoder NN (unsupervised) for dimensionality reduction (a bit like PCA) or dimensionality expansion.
- A Learning Vector Quantization NN (LVQ, supervised) for classification.
- A Self-Organizing Map NN (unsupervised, simplified 1-D variation of SOM) for clustering (a bit like k-means).
- A simple Matrix-Associative-Memory NN (MAM, supervised) for storing input-output vector pairs.
For more information see the package documentation.