Bayesian estimation and prediction for stochastic processes based on the Euler approximation. Considered processes are: jump diffusion, (mixed) diffusion models, hidden (mixed) diffusion models, non-homogeneous Poisson processes (NHPP), (mixed) regression models for comparison and a regression model including a NHPP.
| Version: | 0.1 |
| Depends: | stats, methods, graphics |
| Published: | 2016-06-07 |
| Author: | Simone Hermann |
| Maintainer: | Simone Hermann <hermann at statistik.tu-dortmund.de> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | BaPreStoPro results |
| Reference manual: | BaPreStoPro.pdf |
| Package source: | BaPreStoPro_0.1.tar.gz |
| Windows binaries: | r-devel: BaPreStoPro_0.1.zip, r-release: BaPreStoPro_0.1.zip, r-oldrel: BaPreStoPro_0.1.zip |
| macOS binaries: | r-release: BaPreStoPro_0.1.tgz, r-oldrel: BaPreStoPro_0.1.tgz |
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