Package: sim2Dpredictr 0.1.1
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.
Authors:
sim2Dpredictr_0.1.1.tar.gz
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sim2Dpredictr.pdf |sim2Dpredictr.html✨
sim2Dpredictr/json (API)
NEWS
# Install 'sim2Dpredictr' in R: |
install.packages('sim2Dpredictr', repos = c('https://jmleach-bst.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jmleach-bst/sim2dpredictr/issues
Last updated 9 months agofrom:339a4aef8e. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 26 2024 |
R-4.5-win | NOTE | Oct 26 2024 |
R-4.5-linux | NOTE | Oct 26 2024 |
R-4.4-win | NOTE | Oct 26 2024 |
R-4.4-mac | NOTE | Oct 26 2024 |
R-4.3-win | OK | Oct 26 2024 |
R-4.3-mac | OK | Oct 26 2024 |
Exports:beta_builderchol_s2Dpclassify_multiclasscorr_funcorrelation_buildergenerate_gridgenerate_multinom_probsinf_2D_imageneighbors_by_distprecision_builderproximity_buildersample_FP_Powersim_MVN_Xsim_Y_Binary_Xsim_Y_MVN_Xsim2D_binarymapsim2D_RandSet_HPPPwithin_area
Dependencies:clidotCall64dplyrfansigenericsgluelifecyclemagrittrMASSmatrixcalcpillarpkgconfigR6rbibutilsRcppRdpackrlangspamtibbletidyselectutf8vctrswithr