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sim2Dpredictr  

Simulate Outcomes Using Spatially Dependent Design Matrices
View on CRAN: Click here


Download and install sim2Dpredictr package within the R console
Install from CRAN:
install.packages("sim2Dpredictr")

Install from Github:
library("remotes")
install_github("cran/sim2Dpredictr")

Install by package version:
library("remotes")
install_version("sim2Dpredictr", "0.1.1")



Attach the package and use:
library("sim2Dpredictr")
Maintained by
Justin Leach
[Scholar Profile | Author Map]
First Published: 2020-03-14
Latest Update: 2023-04-03
Description:
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.
How to cite:
Justin Leach (2020). sim2Dpredictr: Simulate Outcomes Using Spatially Dependent Design Matrices. R package version 0.1.1, https://cran.r-project.org/web/packages/sim2Dpredictr. Accessed 18 Apr. 2025.
Previous versions and publish date:
0.1.0 (2020-03-14 17:10)
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Complete documentation for sim2Dpredictr
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