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:Justin Leach [aut, cre, cph]

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'))

Peer review:

Bug tracker:https://github.com/jmleach-bst/sim2dpredictr/issues

On CRAN:

2.70 score 2 scripts 127 downloads 18 exports 23 dependencies

Last updated 9 months agofrom:339a4aef8e. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-winNOTEOct 26 2024
R-4.5-linuxNOTEOct 26 2024
R-4.4-winNOTEOct 26 2024
R-4.4-macNOTEOct 26 2024
R-4.3-winOKOct 26 2024
R-4.3-macOKOct 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