Schumaker Shape-Preserving Spline (schumaker)
This is a shape preserving spline which is guaranteed to be monotonic and concave or convex if the data is monotonic and concave or convex. It does not use any optimisation and is therefore quick and smoothly converges to a fixed point in economic dynamics problems including value function iteration. It also automatically gives the first two derivatives of the spline and options for determining behaviour when evaluated outside the interpolation domain.
Fast Compressed Neural Networks for R (FCNN4R)
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).
Estimation and Fit Diagnostics for Generalized Exponential Random Graph Models (GERGM)
Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models (GERGM) via Gibbs sampling or Metropolis Hastings with exponential down weighting.