Simulate Outcomes Using Spatially Dependent Design Matrices [R package sim2Dpredictr version 0.1.1]

Justin Leach

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年04月03日

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