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Offers Bayesian semiparametric density estimation and tail-index estimation for heavy tailed data, by using a parametric, tail-respecting transformation of the data to the unit interval and then modeling the transformed data with a purely nonparametric logistic Gaussian process density prior. Based on Tokdar et al. (2022) <doi:10.1080/01621459.2022.2104727>.
| Version: | 1.0-1 |
| Depends: | R (≥ 2.6) |
| Imports: | coda, extremefit |
| Published: | 2024-02-16 |
| Author: | Surya Tokdar |
| Maintainer: | Surya Tokdar <surya.tokdar at duke.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | sbde results |
| Reference manual: | sbde.pdf |
| Package source: | sbde_1.0-1.tar.gz |
| Windows binaries: | r-devel: sbde_1.0-1.zip, r-release: sbde_1.0-1.zip, r-oldrel: sbde_1.0-1.zip |
| macOS binaries: | r-release (arm64): sbde_1.0-1.tgz, r-oldrel (arm64): sbde_1.0-1.tgz, r-release (x86_64): sbde_1.0-1.tgz, r-oldrel (x86_64): sbde_1.0-1.tgz |
| Old sources: | sbde 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.