Covariate-Based Covariance Functions for Nonstationary Spatial Modeling
An S4 class to store information
Creates a coco S4 object
Covariate-based Covariance Functions for Nonstationary Gaussian Proces...
Optimizer for coco objects
Prediction for coco objects
Marginal and conditional simulation of nonstationary Gaussian processe...
Dense covariance function (classic parameterization)
Dense covariance function
Sparse covariance function
Sparse covariance function
Dense covariance function (difference parameterization)
Retrieve AIC
Retrieve BIC
Simple build of boundaries
Simple build of boundaries (v2)
Simple build of boundaries (v3)
Simple build of boundaries
Compute approximate confidence intervals for a coco object
Covariance matrix for "coco" class
Based on a set of predictions computes the Continuous Ranked Probabili...
Based on a specific taper scale (delta), retrieves the density of the ...
Create an efficient design matrix based on a list of aspect models
Retrieve estimates from a fitted coco object
getHessian
Retrieve the loglikelihood value
Based on a set of predictions computes the Log-Score
Builds the necessary input for building covariance matrices
Retrieves the modified inverse of the hessian
GetNeg2loglikelihood
GetNeg2loglikelihoodProfile
GetNeg2loglikelihoodREML
GetNeg2loglikelihoodTaper
GetNeg2loglikelihoodTaperProfile
Fast and simple standardization for the design matrix.
Evaluates the spatially-varying functions from a coco object at locs
Computes the spatial mean of a (fitted) coco object
check whether an R object is a formula
Plot Method for coco objects
Plot log info detailed
Show Method for Coco Class
Summary Method for Coco Class
smoothed-L1 penalization over the covariate-driven parameters
Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.