PLoS ONE · 2023 · 27 citations · 23 references
ComputingBiostatisticsSpatial ModelingSpatial StatisticsStatistical ModelingSpatial Data ModelsSpatial PredictionSpatial Statistical ModelingAreal Data ModelingLattice Data ModelsLikelihood-based InferenceSpatial Analysis ModelsSpatial Mixed ModelsSpatial Random Effects ModelsSpatial RegressionWeighted Least Squares
package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or 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.
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Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text
A Computer Movie Simulating Urban Growth in the Detroit Region
Waldo Tobler · Economic Geography · 1970 · 9.6K citations
Simple Features for R: Standardized Support for Spatial Vector Data
Edzer Pebesma · The R Journal · 2018 · 4.6K citations · Full text
Recovery of inter-block information when block sizes are unequal
H. D. Patterson, R. Thompson · Biometrika · 1971 · 3.8K citations