Simulate Outcomes Using Spatially Dependent Design Matrices

Provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) and Furrer and Sain (2010) ), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user.


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0.1.0 by Justin Leach, a year ago

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Authors: Justin Leach [aut, cre, cph]

Documentation:   PDF Manual  

GPL-3 license

Imports car, ggplot2, MASS, Rdpack, spam, tidyverse, tibble, dplyr, magrittr, matrixcalc

Suggests knitr, rmarkdown, testthat

See at CRAN