Spatial Statistical Modeling and Prediction
Glance at many fitted model objects
Compute AICc of fitted model objects
Compute analysis of variance and likelihood ratio tests of fitted mode...
Augment data with information from fitted model objects
Area Under Receiver Operating Characteristic Curve
Extract fitted model coefficients
Confidence intervals for fitted model parameters
Compute Cook's distance
Create a covariance matrix
Fitted model deviance
Create a dispersion parameter initial object
Extract the model matrix from a fitted model object
Create a dispersion parameter object
Compute the empirical semivariogram
Extract model fitted values
Model formulae
Glance at a fitted model object
Compute leverage (hat) values
Regression diagnostics
Find labels from object
Extract log-likelihood
Perform leave-one-out cross validation
Extract the model frame from a fitted model object
Plot fitted model diagnostics
Model predictions (Kriging)
Print values
Compute a pseudo r-squared
Create a random effects covariance parameter initial object
Create a random effects covariance parameter object
Objects exported from other packages
Extract fitted model residuals
Fit spatial autoregressive models
Fit random forest spatial residual models
Create a spatial covariance parameter initial object
Create a spatial covariance parameter object
Fit spatial generalized autoregressive models
Fit spatial generalized linear models
Fit spatial linear models
Fit random forest spatial residual models
spmodel: Spatial Statistical Modeling and Prediction
Simulate a spatial beta random variable
Simulate a spatial binomial random variable
Simulate a spatial gamma random variable
Simulate a spatial inverse gaussian random variable
Simulate a spatial negative binomial random variable
Simulate a spatial normal (Gaussian) random variable
Simulate a spatial Poisson random variable
Summarize a fitted model object
Tidy a fitted model object
Variability component comparison
Calculate variance-covariance matrix for a fitted model object
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. 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. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.