Package: bvpa 1.0.0
bvpa: Bivariate Pareto Distribution
Implements the EM algorithm with one-step Gradient Descent method to estimate the parameters of the Block-Basu bivariate Pareto distribution with location and scale. We also found parametric bootstrap and asymptotic confidence intervals based on the observed Fisher information of scale and shape parameters, and exact confidence intervals for location parameters. Details are in Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199> "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto distribution with location and scale"; E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation"; Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593> "An Introduction to the Bootstrap"; A P Dempster, N M Laird and D B Rubin (1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete Data via the EM Algorithm".
Authors:
bvpa_1.0.0.tar.gz
bvpa_1.0.0.zip(r-4.5)bvpa_1.0.0.zip(r-4.4)bvpa_1.0.0.zip(r-4.3)
bvpa_1.0.0.tgz(r-4.4-any)bvpa_1.0.0.tgz(r-4.3-any)
bvpa_1.0.0.tar.gz(r-4.5-noble)bvpa_1.0.0.tar.gz(r-4.4-noble)
bvpa_1.0.0.tgz(r-4.4-emscripten)bvpa_1.0.0.tgz(r-4.3-emscripten)
bvpa.pdf |bvpa.html✨
bvpa/json (API)
| # Install 'bvpa' in R: |
| install.packages('bvpa', repos = c('https://biplab44.r-universe.dev', 'https://cloud.r-project.org')) |
- precipitation - Precipitation data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:5900ce7a87. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 01 2024 |
| R-4.5-win | OK | Nov 01 2024 |
| R-4.5-linux | OK | Nov 01 2024 |
| R-4.4-win | OK | Nov 01 2024 |
| R-4.4-mac | OK | Nov 01 2024 |
| R-4.3-win | OK | Nov 01 2024 |
| R-4.3-mac | OK | Nov 01 2024 |
Exports:conf.intvconf.intv3estimatesestimates3intlizintliz3logLmLf1mLf2param.bootparam.boot3pctl.funpseu.logLrbb.bvpa
Dependencies:numDeriv
