Welcome to GeoModels

The GeoModels package provides a comprehensive set of procedures for simulation, estimation, and prediction of spatio-temporal random fields with support for both Gaussian and non-Gaussian distributions.

Key features of the package:

Data types supported:
- Spatial data
- Space-time data with spatial locations possibly changing over time
- Bivariate spatial data with (possibly different) spatial location sites
Domain flexibility: Data can be defined on Euclidean space or on a sphere of arbitrary radius
Rich selection of marginal distributions:
- Continuous distributions (real line):
  - Gaussian
  - Skew-Gaussian
  - Student T
  - Logistic
  - Sinh-arcsinh
  - Two-piece
- Positive continuous distributions:
  - Gamma
  - Weibull
  - LogGaussian
  - LogLogistic
- Discrete distributions:
  - Binomial
  - Negative binomial
  - Poisson
- Circular distributions:
  - Wrapped-Gaussian
Modeling capabilities:
- Parametric models for both regression and dependence analysis through covariance models
- Parametric (bivariate) spatial and spatiotemporal covariance models, including:
  - Matern
  - Generalized Wendland
  - Gneiting model
  - Bivariate Matern
Estimation methods:
- Composite likelihood based on pairs
- Full likelihood (when feasible)
Prediction: Optimal (local) linear prediction (kriging)

Tutorials and Examples

Spatial Data

Distribution	Description	Documentation	Code
Gaussian	Analysis of spatial data using a flexible compactly supported covariance model	PDF	R Code
Gaussian	Analysis of global spatial data on the planet Earth using Gaussian random fields	PDF	R Code
Skew-Gaussian	Analysis of asymmetric spatial data using skew-Gaussian random fields	PDF	R Code
t	Analysis of heavy tails spatial data using t random fields	PDF	R Code
Tukey-h	Analysis of heavy tails spatial data using tukey-h random fields	PDF	R Code
Weibull	Analysis of positive spatial data using Weibull random fields	PDF	R Code
Log-Gaussian	Analysis of positive spatial data using Log-Gaussian random fields	PDF	R Code
Poisson	Analysis of spatial count data using Poisson random fields	PDF	R Code
Binomial	Analysis of spatial discrete data using Binomial random fields	PDF	R Code

SpatioTemporal Data

Distribution	Description	Documentation	Code
Gaussian	Analysis of spatio-temporal data using Gaussian random fields	PDF	R Code
Gaussian	Analysis of spatio-temporal data with spatial locations changing over time	PDF	R Code

Spatial bivariate Data

Distribution	Description	Documentation	Code
Gaussian	Analysis of bivariate spatial data using bivariate Gaussian random fields	PDF	R Code

Real data analysis

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

Resources and Download

GeoModels is actively developed on GitHub. The latest binaries and source code are available from our repository:

GeoModels Package sources for any platform
Reference Manual
Bug reports and issues

Installation Instructions

CRAN Installation

The stable version of GeoModels is available on CRAN. Install it directly in R with:

install.packages("GeoModels")

Publications

GeoModels has been used in the following peer-reviewed publications:

Modelling Point Referenced Spatial Count Data: A Poisson Process Approach

Morales-Navarrete D., Bevilacqua M., Caamaño C., Castro L.M.

Journal of the American Statistical Association, 2022

DOI: 10.1080/01621459.2022.2140053

Nearest neighbours weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-hh random fields estimation

Caamaño-Carrillo C, Bevilacqua M, López C, Morales-Oñate V

Research Square, 2022

DOI: 10.21203/rs.3.rs-2073895/v1

A selective view of climatological data and likelihood estimation

Blasi F., Caamaño C., Bevilacqua M., Furrer R.

Spatial Statistics, 2022

DOI: 10.1016/j.spasta.2022.100596

Unifying Compactly Supported and Matern Covariance Functions in Spatial Statistics

Bevilacqua M., Caamaño C., Porcu E.

Journal of Multivariate Analysis, 2022

DOI: 10.1016/j.jmva.2022.104949

A class of random fields with two-piece marginal distributions for modeling point-referenced data with spatial outliers

Bevilacqua M., Caamaño C., Arellano-Valle R. B., Camilo Gomez C.

Test, 2022

DOI: 10.1007/s11749-021-00797-5

On modelling positive continuous data with spatio-temporal dependence

Bevilacqua, M., C. Caamano, and C. Gaetan

Environmetrics, 2020

DOI: 10.1002/env.2628

On Spatial (Skew) t Processes and Applications

Bevilacqua, M., C. Caamano, R. B. A. Valle, and V. Morales-Oñate

Scandinavian Journal of Statistics, 2019

DOI: 10.1111/sjos.12328

Estimation and prediction using generalized Wendland covariance function under fixed domain asymptotics

Bevilacqua, M., T. Faouzi, R. Furrer, and E. Porcu

The Annals of Statistics, 2019

DOI: 10.1214/18-AOS1709

Estimating covariance functions of multivariate skew-gaussian random fields on the sphere

Alegria, A., S. Caro, M. Bevilacqua, E. Porcu, and J. Clarke

Spatial Statistics, 2017

DOI: 10.1016/j.spasta.2017.05.003

Likelihood-based inference for multivariate space-time wrapped-Gaussian fields

Alegria A., Bevilacqua, M., Porcu, E.

Journal of Statistical Computation and Simulation, 2016

DOI: 10.1080/00949655.2015.1122708

Composite likelihood inference for multivariate Gaussian random fields

Bevilacqua M., Alegria A., Velandia D., Porcu E.

Journal of Agricultural Biological and Environmental Statistics, 2016

DOI: 10.1007/s13253-016-0254-5

Assessing the significance of the correlation between the components of a bivariate Gaussian random field

Bevilacqua, M., Vallejos, R. and Velandia D.

Environmetrics, 2015

DOI: 10.1002/env.2348

Comparing composite likelihood methods based on pairs for spatial Gaussian random fields

Bevilacqua, M. and C. Gaetan

Statistics and Computing, 2015

DOI: 10.1007/s11222-014-9471-3

Estimating space and space-time covariance functions for large data sets: a weighted composite likelihood approach

Bevilacqua, M., C. Gaetan, J. Mateu, and E. Porcu

Journal of the American Statistical Association, 2012

DOI: 10.1080/01621459.2011.646928

On spatial skew-Gaussian processes and applications

Zhang, H. and A. El-Shaarawi

Environmetrics, 2010

DOI: 10.1002/env.993

Package Citation

To cite GeoModels in publications, use:


                    Bevilacqua M, Morales-Oñate V, Caamaño-Carrillo C, F Cuevas-Pacheco (2024).

                    GeoModels: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis.

                    R package version 2.1.8, https://CRAN.R-project.org/package=GeoModels.

BibTeX entry for LaTeX users:


                @Manual{GM2024,

                title = {GeoModels: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis},

                author = {Moreno Bevilacqua and Víctor Morales-Oñate and Christian Caamaño-Carrillo},

                year = {2024},

                note = {R package version 2.0.1},

                url = {https://CRAN.R-project.org/package=GeoModels},

                }

About the Authors

Moreno Bevilacqua

Chile

Associate Professor at the Faculty of Engineering and Science of Adolfo Ibañez University in Viña del Mar (Chile). His main research interests concern theory, methodology and applications in multivariate spatio-temporal statistics.

Personal site

Víctor Morales-Oñate

Pelileo-Ecuador

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.

Personal site

Christian Caamaño-Carrillo

Concepción-Chile

Assistant Professor at the Statistics Department of Universidad del Bío-Bío since 2013. He received his PhD in Statistics from Universidad de Valparaíso in 2018. His main research interests concern theory, methodology and applications in space-time statistics for non-Gaussian data.

Research profile

Francisco Cuevas-Pacheco

Valparaíso-Chile

A young Chilean researcher (1987) living in Valparaíso. His research topics include spatio and spatio-temporal point process and random fields, simulation methods, and computational statistics. He loves music, playing guitar, learning languages and sports.

Personal site

Back to Home