This paper briefly describes geostatistical models for Gaussian and non-Gaussian data and demonstrates the geostatsp and dieasemapping packages for performing inference using these models. Making use of R’s spatial data types, and raster objects in particular, makes spatial analyses using geostatistical models simple and convenient. Examples using real data are shown for Gaussian spatial data, binomially distributed spatial data, a logGaussian Cox process, and an area-level model for case counts
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
AbstractGaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two dist...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
Geostatistics is a scientific field which provides methods for processing spatial data. In our proj...
Conventional geostatistical methodology solves the problem of predicting the realized value of a lin...
Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomen...
This article illustrates usage of the ramps R package, which implements the reparameterized and marg...
This volume is the first book-length treatment of model-based geostatistics. The text is expository,...
When fitting a binomial geostatistical model to data obtained by spatially discrete sampling, techni...
Sampling models for geostatistical data are usually based on Gaussian processes. However, real data ...
This article illustrates usage of the ramps R package, which implements the reparameterized and marg...
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
AbstractGaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two dist...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
Geostatistics is a scientific field which provides methods for processing spatial data. In our proj...
Conventional geostatistical methodology solves the problem of predicting the realized value of a lin...
Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomen...
This article illustrates usage of the ramps R package, which implements the reparameterized and marg...
This volume is the first book-length treatment of model-based geostatistics. The text is expository,...
When fitting a binomial geostatistical model to data obtained by spatially discrete sampling, techni...
Sampling models for geostatistical data are usually based on Gaussian processes. However, real data ...
This article illustrates usage of the ramps R package, which implements the reparameterized and marg...
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
AbstractGaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two dist...
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be c...