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RandomFields (version 3.1.16)

RMmodels Overview: Overview over classes of RMmodels

Description

Various classes of models RMxxx are implemented in RandomFields, that have their own man pages. Here an overview over these man pages are given

Arguments

Man pages

Beginners should start with RMmodels, then go for RMmodelsAdvanced if more information is needed.
RMmodels
general introduction and a collection of simple models
RMmodelsAdvanced
includes more advanced stationary and isotropic models, variogram models, non-stationary models and trend models
Bayesian
hierarchical models
RMmodelsMultivariate
multivariate covariance models and multivariate trend models
RMmodelsNonstationary
non-stationary covariance models
RMmodelsMultivariate
multivariate covariance models and multivariate trend models
RMmodelsSpaceTime
space-time covariance models
Spherical models
models based on the polar coordinate system, usually used in earth models
Tail correlation functions
models related to max-stable random fields
trend modelling
how to pass trend specifications
Mathematical functions
simple mathematical functions that typically used to build non-stationary covariance models and arbitrary trends
RMmodelsAuxiliary
rather specialised models, most of them not having positive definiteness property, but used internally in certain simulation algorithms, for instance.

See Also

RC, RR RF, R.

Examples

Run this code
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again

RFgetModelNames(type="positive definite", domain="single variable",
                isotropy="isotropic", operator=!FALSE) ## RMmodel.Rd



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