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RandomFields (version 3.1.36)

Simulation and Analysis of Random Fields

Description

Methods for the inference on and the simulation of Gaussian fields are provided, as well as methods for the simulation of extreme value random fields.

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Version

Install

install.packages('RandomFields')

Monthly Downloads

352

Version

3.1.36

License

GPL (>= 3)

Maintainer

Martin Schlather

Last Published

December 25th, 2016

Functions in RandomFields (3.1.36)

Obsolete Functions

Obsolete functions

Internal functions

Internal functions

Coordinate systems

Coordinate systems

Documentation of some further changings

conventional2RFspDataFrame

Coercion to class 'RFsp' objects

Covariance models for multivariate and vector valued fields

Papers involving RandomFields and co-authored by M. Schlather

Brown-Resnick-Specific

Simulation methods for Brown-Resnick processes

Hierarchical Modelling

Bayesian Spatial Modelling

Fast and Exact Simulation of Large Gaussian Lattice Systems in R2

Evaluation operators (RF commands)

Linear part of RMmodel

Covariance Models for Random Vector Fields

Systematic co-occurrence of tail correlation functions among max-stable processes

On some covariance models based on normal scale mixtures

Information about the implemented covariance models

Models for stationary max-stable random fields

Mathematial C functions

Transformation of coordinate systems

RFempiricalvariogram

Empirical (Cross-)Variogram

RFempVariog-class

Class RFempVariog

Optimisers for fitting model parameters to spatial data

RFfractaldimension

Evaluate Covariance and Variogram Functions

Evaluating distribution families

Hurst coefficient

Fitting model parameters to spatial data (regionalised variables) and to linear (mixed) models

Interpolation methods

RFspatialPointsDataFrame-class

Class "RFspatialPointsDataFrame"

Modifications of the Cauchy Family Covariance Model

Circular Covariance Model

RFgetModelNames

Names of implemented covariance and variogram models

Information on RMmodels

RFsimulate.more.examples

Further Examples for the Simulation of Random Fields

Simulation of Random Fields

Fixed Covariance Matrix

Fractionally Differenced Process Model

RandomFields-package

Simulation and Analysis of Random Fields

Constants used in RandomFields (RC constants)

RFgetMethodNames

Simulation Techniques

Linear part of RMmodel

Internally stored model

RFloglikelihood

Likelihood and estimation of linear models

Dagum Covariance Model Family

Covariance Matrix Constant in Space

Exponentially Damped Cosine

Gneiting Covariance Model

Gneiting-Wendland Covariance Models

RMmodelgenerator-class

Class RMmodelgenerator

Coordinates in the mixed model representation

RMmodelsAdvanced

Advanced features of the mdoels

Generalized Cauchy Family Covariance Model

Generalized Fractal Brownian Motion Variogram Model

User Defined Function

Internal models

Wave Covariance Model / Cardinal Sine

Max-stable random fields

Simulation of Max-Stable Random Fields

RMmodelsNonstationary

Nonstationary features of the mdoels

Ma-Stein operator

Integral exponential operator

RMmodelsSpacetime

Space-time Covariance Models

Quasi-arithmetic mean

Max-stable random fields, advanced

Simulation examples of advanced Max-Stable Random Fields

Simulation of Random Fields

ExtremalGaussian

Extremal Gaussian process

Trend Modelling

Trend Modelling

The Sinepower Covariance Model on the Sphere

Mixture of shape functions

Brown-Resnick process

The Spherical Covariance Model

Simulation of a Chi2 Random Fields

RFgridDataFrame-class

Class RFgridDataFrame

Graphical User Interface For Fitting Covariance Models And Variograms

RFsimulate.sophisticated.examples

Sophisticated Examples for the Simulation of Random Fields

RFsimulateAdvanced

Simulation of Random Fields -- Advanced

Model bridging stationary and intrinsically stationary processes

Covariance Model for binary field based on a Gaussian field

Modified DeWijsian Variogram Model

Plain scalar product

Strokorb's Functions

Tail correlation function of the Brown-Resnick process

Simulation of a T Random Fields

Turning Bands method

Random scaling used with balls

Random scaling used with balls

Random scaling used with balls

Graphical parameters for plots

Random scaling used with balls

RFpointsDataFrame-class

Class RFpointsDataFrame

Space-time moving average model

Exponential Covariance Model

Class RMmodel

Special models for rotation like fields

Generalized Hyperbolic Covariance Model

Generalized Cauchy Family Covariance Model

Identical Model

Local-Global Distinguisher Family Covariance Model

Identical Model

Multiplication of Random Field Models

The Multiquadric Family Covariance Model on th Sphere

Power operator for Variograms and Covariance functions

Turning Bands Method

Transformation of coordinate systems

Plain scalar product

Simulation of Gaussian Random Fields

Hyperplane method

Methods that are specific to certain covariance models

RMmodelFit-class

Class "RMmodelFit"

Penta Covariance Model

Addition of Random Field Models

Models for classes of random fields (RP commands)

RFoptionsAdvanced

Setting control arguments of RandomFields -- advanced examples

RMmodels Overview

Overview over classes of RMmodels

Bessel Family Covariance Model

Anisotropy matrix given by angle

Cauchy Family Covariance Model

Spectral turning bands method

Model for covariates

Pressure and temperature forecast errors over the Pacific Northwest

Transformation of an 'sp' object to an 'RFsp' object

Fractal Gaussian Model Family

Cubic Covariance Model

RMwhittlematern

Whittle-Matern Covariance Model

Gaussian Covariance Model

Matrix operator

Auxiliary and other Models

Creating poylonmial models

RMmodelsMultivariate

Multivariate models

Simulation of Binary Random Fields

Extremal t process

Independent Variables

Method to simulate the Nugget effect

Random scaling used with balls

Stable Family / Powered Exponential Model

Soil data of North Bavaria, Germany

Random coin method

Stein nonseparable space-time model

Circulant Embedding

Circulant Embedding methods

(Mixed) Moving Maxima

Methods relying on square roots of the covariance matrix

Random scaling used with balls