Description This package implements Bayesian hierarchical spatial areal unit models. In such mod-els the spatial correlation is modelled by a set of random effects, which are assigned a condi-tional autoregressive (CAR) prior distribution. Examples of the models in-cluded are the BYM model as well as a recently developed localised spatial smooth-ing model.The creation of this package was supported by the Economic and Social Re-search Council (ESRC) grant RES-000-22-4256. License GPL (> = 2
Spatial documentation is exponentially increasing given the availability of Big Data in the Internet...
We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM...
Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, envi...
Conditional autoregressive models are commonly used to represent spatial autocorrelation in data rel...
Conditional autoregressive models are commonly used to represent spatial autocorrelation in data rel...
Conditional autoregressive (CAR) models are commonly used to capture spatial correlation in areal un...
The popularity of Bayesian disease mapping is increasing, as is the variety of available models. The...
SIGLEAvailable from British Library Document Supply Centre-DSC:3739.0605(R000234839) / BLDSC - Briti...
Conditional autoregressive (CAR) models are commonly used to cap-ture spatial correlation in areal u...
Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, envi...
We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear ...
In the past decade conditional autoregressive modelling specifications have found considerable appli...
This work compares several hierarchical Bayesian techniques for modelling risk surfaces by multivari...
Several different hierarchical Bayesian models can be used for the estimation of spatial risk patter...
The geostan R package supports a complete spatial analysis workflow with Bayesian models for areal d...
Spatial documentation is exponentially increasing given the availability of Big Data in the Internet...
We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM...
Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, envi...
Conditional autoregressive models are commonly used to represent spatial autocorrelation in data rel...
Conditional autoregressive models are commonly used to represent spatial autocorrelation in data rel...
Conditional autoregressive (CAR) models are commonly used to capture spatial correlation in areal un...
The popularity of Bayesian disease mapping is increasing, as is the variety of available models. The...
SIGLEAvailable from British Library Document Supply Centre-DSC:3739.0605(R000234839) / BLDSC - Briti...
Conditional autoregressive (CAR) models are commonly used to cap-ture spatial correlation in areal u...
Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, envi...
We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear ...
In the past decade conditional autoregressive modelling specifications have found considerable appli...
This work compares several hierarchical Bayesian techniques for modelling risk surfaces by multivari...
Several different hierarchical Bayesian models can be used for the estimation of spatial risk patter...
The geostan R package supports a complete spatial analysis workflow with Bayesian models for areal d...
Spatial documentation is exponentially increasing given the availability of Big Data in the Internet...
We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM...
Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, envi...