Depends: raster, sp,OpenStreetMap, R (>= 2.10)
Author: Patrick Brown <patrick.brown@utoronto.ca>, Peter Diggle, Paulo Ribeiro

Depends: Matrix, raster, sp, OpenStreetMap, R (>= 3.0.0)
Imports: RandomFields, geoR, geonames, pracma, spatstat
Description: This package provides to geostatistical modelling facilities using raster and SpatialPoints objects.

Depends: Matrix, raster, sp, R (>= 3.0.0)
Imports: RandomFields, geoR, geonames, pracma, spatstat, rgdal
Author: Patrick Brown <patrick.brown@utoronto.ca>, Barry Rowlingson

Suggests: pracma, geoR, RandomFields, rgdal
Author: Patrick Brown <patrick.brown@utoronto.ca>[aut, cre], Robert Hijmans [ctb]

Depends: methods, Matrix, raster, sp, R (>= 3.0.0)

Suggests: pracma, geoR, RandomFields, rgdal, parallel, numDeriv

Suggests: geoR, RandomFields, rgdal, parallel

Imports: abind, numDeriv, pracma, methods
Suggests: geoR, RandomFields, rgdal, parallel, mapmisc, ellipse

Description: This package provides to geostatistical modelling facilities using raster and SpatialPoints objects.  Non-Gaussian models are fit using INLA, obtainable from http://www.r-inla.org.

Title: Geostatistical Modelling with Likelihood and Bayes
Description: This package provides to geostatistical modelling facilities using Raster and SpatialPoints objects.  Non-Gaussian models are fit using INLA.

Description: Geostatistical modelling facilities using Raster and SpatialPoints objects are provided.  Non-Gaussian models are fit using INLA, and Gaussian geostatistical models use Maximum Likelihood Estimation..

Depends: Matrix (>= 1.2.0), raster, sp, R (>= 3.0.0)
Imports: abind, numDeriv, methods, stats
Suggests: geoR, RandomFields, rgdal, parallel, mapmisc, ellipse, pracma
Description: Geostatistical modelling facilities using Raster and SpatialPoints objects are provided.  Non-Gaussian models are fit using INLA, and Gaussian geostatistical models use Maximum Likelihood Estimation.

Suggests: geoR, RandomFields, rgdal, parallel, mapmisc, ellipse,

Suggests: RandomFields, rgdal, parallel, mapmisc, ellipse, pracma,
Enhances: INLA, diseasemapping, geoR, mvtnorm, Ryacas
Author: Patrick Brown <patrick.brown@utoronto.ca>[aut, cre], Robert Hijmans
Description: Geostatistical modelling facilities using Raster and SpatialPoints
objects are provided. Non-Gaussian models are fit using INLA, and Gaussian
geostatistical models use Maximum Likelihood Estimation.