BaPreStoPro: Bayesian Prediction of Stochastic Processes

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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: Bayesian
CRAN checks: BaPreStoPro results


Reference manual: BaPreStoPro.pdf


Package source: BaPreStoPro_0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BaPreStoPro_0.1.tgz, r-oldrel (arm64): BaPreStoPro_0.1.tgz, r-release (x86_64): BaPreStoPro_0.1.tgz, r-oldrel (x86_64): BaPreStoPro_0.1.tgz


Please use the canonical form to link to this page.