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 uab.edu> BugReports: https://github.com/jmleach-bst/sim2Dpredictr License: GPL-3 URL: https://github.com/jmleach-bst/sim2Dpredictr NeedsCompilation: no Materials: README NEWS CRAN checks: sim2Dpredictr results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=sim2Dpredictr to link to this page.
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