Analysis 1 - Implemention of NNGP models using \(N\) x \(N\) matrices, and examples with \(N=6\), \(N=500\), and \(N=2000\) models. Shows agreement between full GP model and NNGP approximation.
Analysis 2 - New NNGP implementation now only uses \(N\) x \(k\) matrices, which significantly improves NNGP efficiency. Compares NNGP and full GP models for \(N=2000\) and \(N=5000\) models, shows the NNGP is 30-50 times more efficient than the full GP.
Analysis 3 - Implementation showing how to fit full GP model using MCMC and simultaneously make predictions at new points. Two examples are shown, first with \(N=p=5\) shows proof of concept, but is not very efficient. Second, with \(N=50\) and \(p=2500\) is implemented as efficiently as I could.
Analysis 4 - Implements conjugate Gibbs updating of \(\beta\) mean regression coefficients for NNGP model.
Analysis 5 - An example of using the non-stationary correlation model.
FNS-M2 Model - Reproduces an analysis of the precipitation data set used by Paciorek and Schervish (2006), here using a GP spatial model with a nonstationary covariance function.
Sparse General Vecchia (SGV) Implementation - Demonstration of using the sparse general Vecchia implementation of NNGP, as described in Katzfuss and Guinness (2017).