FRK is an R software package for spatial/spatio-temporal modeling and prediction with large datasets. It facilitates optimal spatial prediction (kriging) on the most commonly used manifolds (in Euclidean space and on the surface of the sphere), for both spatial and spatio-temporal fields. It differs from many of the packages for spatial modeling and prediction by avoiding stationary and isotropic covariance and variogram models, instead constructing a spatial random effects (SRE) model on a fine-resolution discretized spatial domain. The discrete element is known as a basic areal unit (BAU), whose intro-duction in the software leads to several practical advantages. The software can be used to (i) integrate multiple observations with differe...
Copyright ©CSIRO 2010 This is a detailed set of notes for a workshop on Analysing spatial point patt...
Automatic generation and selection of spatial predictors for spatial regression with Random Forest. ...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...
Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and their analysi...
Spatial prediction is commonly achieved under the assumption of a Gaussian random field (GRF) by obt...
<p>The spatial random effects model is flexible in modeling spatial covariance functions and is comp...
Random forest and similar Machine Learning techniques are already used to generate spatial predictio...
In this document, we describe Fixed Rank Kriging (FRK), an approach to the analysis of very large sp...
Spatial predictive methods are increasingly being used to generate predictions across various discip...
The article describes the R-package constrainedKriging, a tool for spatial prediction problems that ...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotr...
In this article, we review and compare a number of methods of spatial prediction, where each method ...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...
Copyright ©CSIRO 2010 This is a detailed set of notes for a workshop on Analysing spatial point patt...
Automatic generation and selection of spatial predictors for spatial regression with Random Forest. ...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...
Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and their analysi...
Spatial prediction is commonly achieved under the assumption of a Gaussian random field (GRF) by obt...
<p>The spatial random effects model is flexible in modeling spatial covariance functions and is comp...
Random forest and similar Machine Learning techniques are already used to generate spatial predictio...
In this document, we describe Fixed Rank Kriging (FRK), an approach to the analysis of very large sp...
Spatial predictive methods are increasingly being used to generate predictions across various discip...
The article describes the R-package constrainedKriging, a tool for spatial prediction problems that ...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotr...
In this article, we review and compare a number of methods of spatial prediction, where each method ...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...
Copyright ©CSIRO 2010 This is a detailed set of notes for a workshop on Analysing spatial point patt...
Automatic generation and selection of spatial predictors for spatial regression with Random Forest. ...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...