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Draw samples from the direct sampling spatial prior model as described in G. White, D. Sun, P. Speckman (2019) <doi:10.48550/arXiv.1906.05575>. The basic model assumes a Gaussian likelihood and derives a spatial prior based on thin-plate splines.
Version: | 0.1.1 |
Imports: | mcmcse, posterior, rust, sp |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | cowplot, ggplot2, gstat, interp, knitr, rmarkdown, testthat (≥ 3.0.0) |
Published: | 2022-07-12 |
DOI: | 10.32614/CRAN.package.DSSP |
Author: | Gentry White [aut, cre], Rex Parsons [aut] |
Maintainer: | Gentry White <gentry.white at qut.edu.au> |
BugReports: | https://github.com/gentrywhite/DSSP/issues |
License: | GPL (≥ 3) |
URL: | https://github.com/gentrywhite/DSSP |
NeedsCompilation: | yes |
Materials: | README NEWS |
CRAN checks: | DSSP results |
Reference manual: | DSSP.pdf |
Vignettes: |
dssp |
Package source: | DSSP_0.1.1.tar.gz |
Windows binaries: | r-devel: DSSP_0.1.1.zip, r-release: DSSP_0.1.1.zip, r-oldrel: DSSP_0.1.1.zip |
macOS binaries: | r-release (arm64): DSSP_0.1.1.tgz, r-oldrel (arm64): DSSP_0.1.1.tgz, r-release (x86_64): DSSP_0.1.1.tgz, r-oldrel (x86_64): DSSP_0.1.1.tgz |
Old sources: | DSSP archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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