Robust Geostatistical Analysis of Spatial Data
Compact Storage of Symmetric and Triangular Matrices
Control Parameters for georob
Cross-Validating a Spatial Linear Model Fitted by georob
Generic Cross-validation
Fitting Model Functions to Sample Variograms
Variogram Models
Robust Fitting of Spatial Linear Models
S3 Methods for Stepwise Building Fixed-Effects Models for Class `georo...
Fitted georob Object
The georob Package
Common S3 Methods for Class georob
Simulating Realizations of Gaussian Processes
Internal Functions of Package georob
Unbiased Back-Transformations for Log-normal Kriging
Setting Default Values of Variogram Parameters
Names and Permissible Ranges of Variogram Parameters
Plot Methods for Class georob
Parallelized Matrix Multiplication
Predict Method for Robustly Fitted Spatial Linear Models
Profile Likelihood
Re-Exported Functions from package imports
Computing (Robust) Sample Variograms of Spatial Data
Summary Statistics of (Cross-)Validation Prediction Errors
Provides functions for efficiently fitting linear models with spatially correlated errors by robust (Kuensch et al. (2011) <doi:10.3929/ethz-a-009900710>) and Gaussian (Harville (1977) <doi:10.1080/01621459.1977.10480998>) (Restricted) Maximum Likelihood and for computing robust and customary point and block external-drift Kriging predictions (Cressie (1993) <doi:10.1002/9781119115151>), along with utility functions for variogram modelling in ad hoc geostatistical analyses, model building, model evaluation by cross-validation, (conditional) simulation of Gaussian processes (Davies and Bryant (2013) <doi:10.18637/jss.v055.i09>), unbiased back-transformation of Kriging predictions of log-transformed data (Cressie (2006) <doi:10.1007/s11004-005-9022-8>).