sim2Dpredictr: 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) <doi:10.1111/1467-9868.00288> and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), 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.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: MASS, Rdpack, spam (≥ 2.2-0), tibble, dplyr, matrixcalc
Suggests: knitr, rmarkdown, testthat, V8
Published: 2023-04-03
DOI: 10.32614/CRAN.package.sim2Dpredictr
Author: Justin Leach [aut, cre, cph]
Maintainer: Justin Leach <jleach at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: sim2Dpredictr results


Reference manual: sim2Dpredictr.pdf


Package source: sim2Dpredictr_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sim2Dpredictr_0.1.1.tgz, r-oldrel (arm64): sim2Dpredictr_0.1.1.tgz, r-release (x86_64): sim2Dpredictr_0.1.1.tgz, r-oldrel (x86_64): sim2Dpredictr_0.1.1.tgz
Old sources: sim2Dpredictr archive


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