Package: ref.ICAR 2.0.2

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 [aut, cre], Matthew J. Keefe [aut], Christopher T. Franck [aut], Marco A.R. Ferreira [aut]

ref.ICAR_2.0.2.tar.gz
ref.ICAR_2.0.2.tar.gz(r-4.5-noble)ref.ICAR_2.0.2.tar.gz(r-4.4-noble)
ref.ICAR_2.0.2.tgz(r-4.4-emscripten)ref.ICAR_2.0.2.tgz(r-4.3-emscripten)
ref.ICAR.pdf |ref.ICAR.html
ref.ICAR/json (API)

# Install 'ref.ICAR' in R:
install.packages('ref.ICAR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

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

2.70 score 164 downloads 7 exports 59 dependencies

Last updated 24 days agofrom:47e723465b. Checks:2 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 22 2025
R-4.5-linuxOKJan 22 2025

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

Dependencies:apebootclassclassIntclicodacolorspacecorpcorcubatureDBIdeldirdigestdplyre1071fansifarvergenericsggplot2gluegtablegtoolsisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixMCMCglmmmgcvmunsellmvtnormnlmepillarpkgconfigpracmaproxyR6rbibutilsRColorBrewerRcppRdpackrlangs2scalessfspspDataspdeptensorAtibbletidyselectunitsutf8vctrsviridisLitewithrwk

Applying an ICAR reference prior

Rendered fromref-icar-vignette.Rmdusingknitr::rmarkdownon Jan 22 2025.

Last update: 2025-01-22
Started: 2018-11-19