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 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:
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