We describe the R package geoCount for the analysis of geostatistical count data. The package performs Bayesian analysis for the Poisson-lognormal and binomial-logitnormal spatial models, which are subclasses of the class of generalized linear spatial models proposed by Diggle, Tawn, and Moyeed (1998). The package implements the computational intensive tasks in C++ using an R/C++ interface, and has parallel computation capabilities to speed up the computations. geoCount also implements group updating, Langevin- Hastings algorithms and a data-based parameterization, algorithmic approaches proposed by Christensen, Roberts, and Sköld (2006) to improve the efficiency of the Markov chain Monte Carlo algorithms. In addition, the package includes ...
Though in the last decade many works have appeared in the literature dealing with model-based extens...
The geostan R package supports a complete spatial analysis workflow with Bayesian models for areal d...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
We describe the R package geoCount for the analysis of geostatistical count data. The package perfor...
This work describes the R package gcKrig for the analysis of geostatistical count data using Gaussia...
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
This paper briefly describes geostatistical models for Gaussian and non-Gaussian data and demonstrat...
Geostatistics is a scientific field which provides methods for processing spatial data. In our proj...
Cartogram drawing is a technique for showing geography-related statistical information, such as demo...
In this paper we introduce a new R package, PrevMap, for the analysis of spatially referenced preval...
This article illustrates usage of the ramps R package, which implements the reparameterized and marg...
With the ability to collect and store increasingly large datasets on modern computers comes the need...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation...
In this paper we introduce a new R package, PrevMap, for the analysis of spatially referenced preval...
Though in the last decade many works have appeared in the literature dealing with model-based extens...
The geostan R package supports a complete spatial analysis workflow with Bayesian models for areal d...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
We describe the R package geoCount for the analysis of geostatistical count data. The package perfor...
This work describes the R package gcKrig for the analysis of geostatistical count data using Gaussia...
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
This paper briefly describes geostatistical models for Gaussian and non-Gaussian data and demonstrat...
Geostatistics is a scientific field which provides methods for processing spatial data. In our proj...
Cartogram drawing is a technique for showing geography-related statistical information, such as demo...
In this paper we introduce a new R package, PrevMap, for the analysis of spatially referenced preval...
This article illustrates usage of the ramps R package, which implements the reparameterized and marg...
With the ability to collect and store increasingly large datasets on modern computers comes the need...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation...
In this paper we introduce a new R package, PrevMap, for the analysis of spatially referenced preval...
Though in the last decade many works have appeared in the literature dealing with model-based extens...
The geostan R package supports a complete spatial analysis workflow with Bayesian models for areal d...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...