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It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) <doi:10.48550/arXiv.2004.04341>.
| Version: | 1.9 |
| Depends: | R (≥ 3.6.0) |
| Imports: | stats, modeest, cubature, truncdist, invgamma, LaplacesDemon, HDInterval, mvtnorm |
| Published: | 2022-09-11 |
| DOI: | 10.32614/CRAN.package.OBASpatial |
| Author: | Alejandro Ordonez, Marcos O. Prates , Larissa A. Matos, Victor H. Lachos. |
| Maintainer: | Alejandro Ordonez <ordonezjosealejandro at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | OBASpatial results |
| Reference manual: | OBASpatial.pdf |
| Package source: | OBASpatial_1.9.tar.gz |
| Windows binaries: | r-devel: OBASpatial_1.9.zip, r-release: OBASpatial_1.9.zip, r-oldrel: OBASpatial_1.9.zip |
| macOS binaries: | r-release (arm64): OBASpatial_1.9.tgz, r-oldrel (arm64): OBASpatial_1.9.tgz, r-release (x86_64): OBASpatial_1.9.tgz, r-oldrel (x86_64): OBASpatial_1.9.tgz |
| Old sources: | OBASpatial 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.