The FCNN4R package provides an interface to kernel routines
from the FCNN C++ library. FCNN is based on a completely new
Artificial Neural Network representation that offers unmatched
efficiency, modularity, and extensibility. FCNN4R provides
standard teaching (backpropagation, Rprop) and pruning
algorithms (minimum magnitude, Optimal Brain Surgeon), but it is
first and foremost an efficient computational engine. Users
can easily implement their algorithms by taking advantage
of fast gradient computing routines, as well as network
reconstruction functionality (removing weights and redundant
neurons).
R (>= 3.0), stats, graphics, methods, Rcpp

Provides an interface to kernel routines from the FCNN C++ library.
FCNN is based on a completely new Artificial Neural Network
representation that offers unmatched efficiency, modularity,
and extensibility. FCNN4R provides standard teaching
(backpropagation, Rprop, simulated annealing) and pruning algorithms
(minimum magnitude, Optimal Brain Surgeon), but it is first
and foremost an efficient computational engine. Users can easily
implement their algorithms by taking advantage of fast gradient
computing routines, as well as network reconstruction functionality
(removing weights and redundant neurons).

Provides an interface to kernel routines from the FCNN C++ library.
FCNN is based on a completely new Artificial Neural Network representation that
offers unmatched efficiency, modularity, and extensibility. FCNN4R provides
standard teaching (backpropagation, Rprop, simulated annealing, stochastic
gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon),
but it is first and foremost an efficient computational engine. Users can
easily implement their algorithms by taking advantage of fast gradient computing
routines, as well as network reconstruction functionality (removing weights
and redundant neurons, reordering inputs, merging networks). Networks can be
exported to C functions in order to integrate them into virtually any software
solution.