Package: ref.ICAR 2.0.1

Erica M. Porter

ref.ICAR: Objective Bayes Intrinsic Conditional Autoregressive Model for Areal Data

Implements an objective Bayes intrinsic conditional autoregressive prior. This model provides an objective Bayesian approach for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of spatial random effects.

Authors:Erica M. Porter, Matthew J. Keefe, Christopher T. Franck, and Marco A.R. Ferreira

ref.ICAR_2.0.1.tar.gz
ref.ICAR_2.0.1.tar.gz(r-4.5-noble)ref.ICAR_2.0.1.tar.gz(r-4.4-noble)
ref.ICAR_2.0.1.tgz(r-4.4-emscripten)ref.ICAR_2.0.1.tgz(r-4.3-emscripten)
ref.ICAR.pdf |ref.ICAR.html✨
ref.ICAR/json (API)

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

7 exports 0.00 score 59 dependencies 13 scripts 232 downloads

Last updated 1 years agofrom:3b6a7fd915. Checks:OK: 2. Indexed: yes.

Exports:probs.icarref.analysisref.MCMCref.plotref.summaryreg.summaryshape.H

Dependencies:apebootclassclassIntclicodacolorspacecorpcorcubatureDBIdeldirdigestdplyre1071fansifarvergenericsggplot2gluegtablegtoolsisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixMCMCglmmmgcvmunsellmvtnormnlmepillarpkgconfigpracmaproxyR6rbibutilsRColorBrewerRcppRdpackrlangs2scalessfspspDataspdeptensorAtibbletidyselectunitsutf8vctrsviridisLitewithrwk