spmodel0.9.0 package

Spatial Statistical Modeling and Prediction

AICc

Compute AICc of fitted model objects

anova.spmodel

Compute analysis of variance and likelihood ratio tests of fitted mode...

augment.spmodel

Augment data with information from fitted model objects

AUROC

Area Under Receiver Operating Characteristic Curve

coef.spmodel

Extract fitted model coefficients

confint.spmodel

Confidence intervals for fitted model parameters

cooks.distance.spmodel

Compute Cook's distance

covmatrix

Create a covariance matrix

deviance.spmodel

Fitted model deviance

dispersion_initial

Create a dispersion parameter initial object

dispersion_params

Create a dispersion parameter object

esv

Compute the empirical semivariogram

fitted.spmodel

Extract model fitted values

formula.spmodel

Model formulae

glance.spmodel

Glance at a fitted model object

glances

Glance at many fitted model objects

hatvalues.spmodel

Compute leverage (hat) values

influence.spmodel

Regression diagnostics

labels.spmodel

Find labels from object

logLik.spmodel

Extract log-likelihood

loocv

Perform leave-one-out cross validation

model.frame.spmodel

Extract the model frame from a fitted model object

model.matrix.spmodel

Extract the model matrix from a fitted model object

plot.spmodel

Plot fitted model diagnostics

predict.spmodel

Model predictions (Kriging)

print.spmodel

Print values

pseudoR2

Compute a pseudo r-squared

randcov_initial

Create a random effects covariance parameter initial object

randcov_params

Create a random effects covariance parameter object

reexports

Objects exported from other packages

residuals.spmodel

Extract fitted model residuals

spautor

Fit spatial autoregressive models

spautorRF

Fit random forest spatial residual models

spcov_initial

Create a spatial covariance parameter initial object

spcov_params

Create a spatial covariance parameter object

spgautor

Fit spatial generalized autoregressive models

spglm

Fit spatial generalized linear models

splm

Fit spatial linear models

splmRF

Fit random forest spatial residual models

spmodel-package

spmodel: Spatial Statistical Modeling and Prediction

sprbeta

Simulate a spatial beta random variable

sprbinom

Simulate a spatial binomial random variable

sprgamma

Simulate a spatial gamma random variable

sprinvgauss

Simulate a spatial inverse gaussian random variable

sprnbinom

Simulate a spatial negative binomial random variable

sprnorm

Simulate a spatial normal (Gaussian) random variable

sprpois

Simulate a spatial Poisson random variable

summary.spmodel

Summarize a fitted model object

tidy.spmodel

Tidy a fitted model object

varcomp

Variability component comparison

vcov.spmodel

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>.

  • Maintainer: Michael Dumelle
  • License: GPL-3
  • Last published: 2024-11-06