GpGp 0.2.2

Fixed bug in “fit_model” when missing values are present. Updated behavior of Fisher scoring algorithm when information matrix ill-conditioned Allow user to fix a subset of parameters in “fit_model” Allow user to specify maximum number of iterations in “fit_model” New faster computational algorithm for predictions Several new covariance functions, including

matern15_isotropic matern25_isotropic matern35_isotropic matern45_isotropic matern15_scaledim
matern25_scaledim
matern35_scaledim
matern45_scaledim

GpGp 0.2.1

Bug fix for overloaded use of ‘pow’ function in ‘basis.h’

GpGp 0.2.0

This update includes an implementation of the Fisher Scoring algorithm described in this paper https://arxiv.org/abs/1905.08374, computed in a single pass through the data.

Much of the C++ code has been rewritten and reorganized, making use of the Armadillo C++ linear algebra library, with the help of RcppArmadillo.

There are also several new covariance functions. The complete list of covariance functions is now:

matern_isotropic exponential_isotropic matern_spacetime exponential_spacetime matern_scaledim exponential_scaledim matern_anisotropic2D exponential_anisotropic2D exponential_anisotropic3D matern_nonstat_var exponential_nonstat_var matern_sphere exponential_sphere matern_spheretime exponential_spheretime matern_sphere_warp exponential_sphere_warp matern_spheretime_warp exponential_spheretime_warp

GpGp 0.1.1

This is a minor release fixing numerical stability problems that arise during optimization of the likelihood.