Learn R Programming

spmodel: Spatial Statistical Modeling and Prediction

Overview

spmodel is an R package used to fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. Visit our website at https://usepa.github.io/spmodel/.

Installation

Install and load the most recent approved version from CRAN by running

# install the most recent approved version from CRAN
install.packages("spmodel")
# load the most recent approved version from CRAN
library(spmodel)

Install and load the most recent development version ofspmodel from GitHub by running

# Installing from GitHub requires you first install the remotes package
install.packages("remotes")

# install the most recent development version from GitHub
remotes::install_github("USEPA/spmodel", ref = "main")
# load the most recent development version from GitHub
library(spmodel)

Install the most recent development version of spmodel from GitHub with package vignettes by running

install the most recent development version from GitHub with package vignettes
devtools::install_github("USEPA/spmodel", ref = "main", build_vignettes=TRUE)

View the introductory vignette in RStudio by running

vignette("introduction", "spmodel")

We have several other vignettes that are not shipped with CRAN but are available on our website (located at https://usepa.github.io/spmodel/) in the "Articles" tab:

  1. A Detailed Guide to spmodel
  2. Spatial Generalized Linear Models in spmodel
  3. Technical Details

Further detail regarding spmodel is contained in the package's documentation manual.

Citation

If you use spmodel in a formal publication or report, please cite it. Citing spmodel lets us devote more resources to it in the future. View the spmodel citation by running

citation(package = "spmodel")
#> 
#> To cite spmodel in publications use:
#> 
#>   Dumelle M, Higham M, Ver Hoef JM (2023). spmodel: Spatial statistical modeling and prediction in R. PLOS ONE, 18(3): e0282524.
#>   https://doi.org/10.1371/journal.pone.0282524
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {{spmodel}: Spatial statistical modeling and prediction in {R}},
#>     author = {Michael Dumelle and Matt Higham and Jay M. {Ver Hoef}},
#>     journal = {PLOS ONE},
#>     year = {2023},
#>     volume = {18},
#>     number = {3},
#>     pages = {1--32},
#>     doi = {10.1371/journal.pone.0282524},
#>     url = {https://doi.org/10.1371/journal.pone.0282524},
#>   }

Package Contributions

We encourage users submit GitHub issues and enhancement requests so we may continue to improve spmodel.

EPA Disclaimer

The United States Environmental Protection Agency (EPA) GitHub project code is provided on an "as is" basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity , confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.

License

This project is licensed under the GNU General Public License, GPL-3.

Copy Link

Version

Install

install.packages('spmodel')

Monthly Downloads

413

Version

0.11.0

License

GPL-3

Maintainer

Michael Dumelle

Last Published

July 3rd, 2025

Functions in spmodel (0.11.0)

loocv

Perform leave-one-out cross validation
esv

Compute the empirical semivariogram
model.frame.spmodel

Extract the model frame from a fitted model object
fc_borders

Four Corners State Borders
pseudoR2

Compute a pseudo r-squared
print.spmodel

Print values
plot.spmodel

Plot fitted model diagnostics
labels.spmodel

Find labels from object
predict.spmodel

Model predictions (Kriging)
lake

National Lakes Assessment Data
glance.spmodel

Glance at a fitted model object
hatvalues.spmodel

Compute leverage (hat) values
sprgamma

Simulate a spatial gamma random variable
influence.spmodel

Regression diagnostics
model.matrix.spmodel

Extract the model matrix from a fitted model object
moose

Moose counts and presence in Alaska, USA
glances

Glance at many fitted model objects
splmRF

Fit random forest spatial residual models
spglm

Fit spatial generalized linear models
splm

Fit spatial linear models
sulfate_preds

Locations at which to predict sulfate atmospheric deposition in the conterminous USA
moose_preds

Locations at which to predict moose counts and presence in Alaska, USA
sprnbinom

Simulate a spatial negative binomial random variable
spmodel-package

spmodel: Spatial Statistical Modeling and Prediction
summary.spmodel

Summarize a fitted model object
sprnorm

Simulate a spatial normal (Gaussian) random variable
randcov_initial

Create a random effects covariance parameter initial object
sprbinom

Simulate a spatial binomial random variable
randcov_params

Create a random effects covariance parameter object
sprbeta

Simulate a spatial beta random variable
moss

Heavy metals in mosses near a mining road in Alaska, USA
seal

Estimated harbor-seal trends from abundance data in southeast Alaska, USA
spautor

Fit spatial autoregressive models
tidy.spmodel

Tidy a fitted model object
spcov_params

Create a spatial covariance parameter object
varcomp

Variability component comparison
texas

Texas Turnout Data
spgautor

Fit spatial generalized autoregressive models
dispersion_initial

Create a dispersion parameter initial object
dispersion_params

Create a dispersion parameter object
vcov.spmodel

Calculate variance-covariance matrix for a fitted model object
lake_preds

Lakes Prediction Data
logLik.spmodel

Extract log-likelihood
residuals.spmodel

Extract fitted model residuals
reexports

Objects exported from other packages
sprinvgauss

Simulate a spatial inverse gaussian random variable
spautorRF

Fit random forest spatial residual models
spcov_initial

Create a spatial covariance parameter initial object
sulfate

Sulfate atmospheric deposition in the conterminous USA
sprpois

Simulate a spatial Poisson random variable
confint.spmodel

Confidence intervals for fitted model parameters
coef.spmodel

Extract fitted model coefficients
covmatrix

Create a covariance matrix
AUROC

Area Under Receiver Operating Characteristic Curve
deviance.spmodel

Fitted model deviance
caribou

A caribou forage experiment
augment.spmodel

Augment data with information from fitted model objects
cooks.distance.spmodel

Compute Cook's distance
AICc

Compute AICc of fitted model objects
anova.spmodel

Compute analysis of variance and likelihood ratio tests of fitted model objects
fitted.spmodel

Extract model fitted values
formula.spmodel

Model formulae