sim2Dpredictr: Simulate Outcomes Using Spatially Dependent Design Matrices (original) (raw)
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 |
| 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 |
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