Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis
Annual precipitation anomalies in U.S.
Checking Bivariate covariance models
Checking Distance
Checking if a covariance is valid only on the sphere
Checking SpaceTime covariance models
Checking Correlation Model
Checking Input
Checking Composite-likelihood Type
Checking Random Field type
Checking Likelihood Objects
Checking Variance Estimates Type
Optimizes the Composite indipendence log-likelihood
Optimizes the Composite log-likelihood
Optimizes the Composite log-likelihood
Lists the Parameters of a Correlation Model
Lists the Parameters of a Correlation Model
Spatial Anisotropy correction
Spatial and Spatio-temporal correlation or covariance of (non) Gaussia...
Spatial and Spatio-temporal correlation or covariance of (non) Gaussia...
Computes the fitted variogram model.
Image plot displaying the pattern of the sparsness of a covariance mat...
Spatial and Spatio-temporal Covariance Matrix of (non) Gaussian random...
n-fold kriging Cross-validation
Computation of drop-one predictive scores
Max-Likelihood-Based Fitting of Gaussian and non Gaussian random field...
Max-Likelihood-Based Fitting of Gaussian and non Gaussian RFs.
Spatial (bivariate) and spatio temporal optimal linear prediction for ...
Spatial (bivariate) and spatio temporal optimal linear local predictio...
Deleting NA values (missing values) from a spatial or spatio-temporal ...
Spatio (temporal) neighborhood selection for local kriging.
A brute force algorithm for spatial or spatiotemoral optimal neighboor...
Spatial or spatiotemporal near neighbour indices.
GeoNosymindices.
Spatio (temporal) outliers detection
Probability integral or normal score tranformation
Quantile-quantile plot
Computes fitted covariance and/or variogram
h-scatterplot for space and space-time data.
Computation of predictive scores
Simulation of Gaussian and non Gaussian Random Fields.
Fast simulation of Gaussian and non Gaussian Random Fields.
Simulation of Gaussian and non Gaussian Random Fields using copula.
Statistical Hypothesis Tests for Nested Models
Update a GeoFit object using parametric bootstrap for std error esti...
Empirical semi-variogram estimation
WLS of Random Fields
Optimizes the Log Likelihood
Matrix decomposition
Square root, inverse and log determinant of a (semi)positive definite ...
Lists the Nuisance Parameters of a Random Field
Internal function handling Nuisance Parameters of a Random Field
Plot Spatial and Spatio-temporal correlation or covariance of (non) Ga...
Plot empirical spatial, spatio-temporal and spatial bivariate semi-Var...
Circulant embeeding simulation
Extracting information from an sp or spacetime object
Initializes the Parameters for Estimation Procedures
Computes Starting Values based on Weighted Least Squares
Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.