GeoModels: an R Package for Geostatistical Gaussian and non Gaussian Data Analysis

header

Welcome to GeoModels

The GeoModels package provides a set of procedures for simulation, estimation and prediction of spatio-temporal random fields.

The main features of the package are:

Tutorials and Examples

Spatial Data

Distribution Description Link Code
Gaussian Analysis of spatial data using a flexible compactly supported covariance model Link Wend_Matern.R
Gaussian Analysis of global spatial data on the planet Earth using Gaussian random fields Link gausphere.R
Skew-Gaussian Analysis of asymmetric spatial data using skew-Gaussian random fields Link skewg.R
t Analysis of heavy tails spatial data using t random fields Link t.R
Weibull Analysis of positive spatial data using Weibull random fields Link weibull.R
Poisson Analysis of positive spatial count data using Poisson random fields Link poisson.R

SpatioTemporal Data

Distribution Description Link Code
Gaussian Analysis of (large) spatio-temporal data using Gaussian random fields Link ST.R
Gaussian Analysis of spatio-temporal data with spatial locations changing over time Link ST_dyn.R

Spatial bivariate Data

Distribution Description Link Code
Gaussian Analysis of bivariate spatial data using bivariate Gaussian random fields Link biv.R

Real data analysis

Distribution Description Link Code
Skew-Gaussian Analysis of spatial precipitation anomalies using Gaussian and skew Gaussian random fields Link anomalies.R
t Analysis of maximum temperature of Australia using t random fields Link austr.R

Resources and Download

Latest binaries and sources for GeoModels are availables from GitHub repository:

Installation Instructions

We currently are loaded in Github only. This means that for GeoModels installation you will need to previously install devtools package if you do not have it installed yet:

install.packages("devtools")
library(devtools)

devtools lets you install packages from github since they need to be installed from source code.

We have developed two GeoModels version, one standard version and one that uses the OpenCL framework for parallel computing. The standard version can be installed in any operating system: Windows, OSX and Linux,

install_github("vmoprojs/GeoModels", quiet = TRUE)
library(GeoModels)

and you are good to go.

A word of caution though. In Windows, make sure RTools is installed in order to build packages. Rtools must be compatible with your current R version. This usually happens with Rtools latest release (which is Rtools.35.exe today), you can download Rtools from here. Also, we have lately received some issues regarding devtools since the last release has a slight problem. To avoid this, install version 1.13.6 that you can find here

The OpenCL GeoModels version is currently supported for OSX (Sierra and Mojave).

All OpenCL code has been tested on Intel(R) Core(TM) i7-4980HQ CPU @ 2.80GHz. We are currently developing the OpenCL GeoModels version to work in any operating system and debbuging it for other graphics cards. It is installed with this code:

install_github("vmoprojs/GeoModels-OCL", quiet = TRUE)
library(GeoModels)

Notice that in Apple, XCode must be also installed required headers are installed. We also recommend to install gpuR package and try an example from that package so that OpenCL headers are tested. Installation instructions are in this link.

Publications

Associated publications:

Package Citation

Once you have installed GeoModels, you can have a BibTex citation with citation("GeoModels") and get:

To cite package ‘GeoModels’ in publications use:
Moreno Bevilacqua and Víctor Morales-Oñate and Christian Caamaño-Carrillo (2018). GeoModels: A Package for Geostatistical Gaussian and non Gaussian Data Analysis.
R package version 1.0.3-4. https://vmoprojs.github.io/GeoModels-page/

A BibTeX entry for LaTeX users is
@Manual{GM2018,
title = {GeoModels: A Package for Geostatistical Gaussian and non Gaussian Data Analysis},
author = {Moreno Bevilacqua and Víctor Morales-Oñate and Christian Caamaño-Carrillo},
year = {2018},
note = {R package version 1.0.3-4},
url = {https://vmoprojs.github.io/GeoModels-page/},
}

About the authors

Moreno Bevilacqua

Chile

Moreno Bevilacqua is an Associate Professor at the Faculty of Engineering and Science of Adolfo Ibañez University in Viña del Mar (Chile) from August 2020. He worked at the Statistics Department of University of Valparaiso (Chile) from August 2012. He has carried out research as: a post-doc at the Department of Statistics, University Ca' Foscari of Venice from May 2008 to December 2010, a research fellow at the University of Bergamo from January 2011 to July 2012.
He received his PhD in Statistics in 2008 and his Degree in Statistics in 2001 from the University of Padua. His main research interests concern theory, methodology and applications in multivariate spatio-temporal statistics.

Personal site

Víctor Morales-Oñate

Pelileo-Ecuador

Víctor received his PhD in Statistics from Universidad de Valparaíso-Chile in 2018. His main research interests concern computational spatial (temporal) geostatistics applications.
Other interests include data analytics in business, machine learning, quantitative economy and philosophy, particularly, Bertrand Russell's political ideas.

Personal site

Christian Caamaño-Carrillo

Concepción-Chile

Christian Caamaño-Carrillo is an Assistant Professor at the Statistics Department of Universidad del Bío-Bío from 2013. He received his PhD in Statistics from Universidad de Valparaíso in 2018 and his Master's in Statistics in 2012.
His main research interests concern theory, methodology and applications in space-time statistics for non-Gaussian data.

Personal site

HOME