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.