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Routines for state estimate in a linear Gaussian state space model and a simple stochastic volatility model using particle filtering. Parameter inference is also carried out in these models using the particle Metropolis-Hastings algorithm that includes the particle filter to provided an unbiased estimator of the likelihood. This package is a collection of minimal working examples of these algorithms and is only meant for educational use and as a start for learning to them on your own.
| Version: | 1.5 |
| Depends: | R (≥ 3.2.3) |
| Imports: | mvtnorm, Quandl, grDevices, graphics, stats |
| Published: | 2019-03-22 |
| Author: | Johan Dahlin |
| Maintainer: | Johan Dahlin <uni at johandahlin.com> |
| License: | GPL-2 |
| URL: | https://github.com/compops/pmh-tutorial-rpkg |
| NeedsCompilation: | no |
| Citation: | pmhtutorial citation info |
| CRAN checks: | pmhtutorial results |
| Reference manual: | pmhtutorial.pdf |
| Package source: | pmhtutorial_1.5.tar.gz |
| Windows binaries: | r-devel: pmhtutorial_1.5.zip, r-release: pmhtutorial_1.5.zip, r-oldrel: pmhtutorial_1.5.zip |
| macOS binaries: | r-release (arm64): pmhtutorial_1.5.tgz, r-oldrel (arm64): pmhtutorial_1.5.tgz, r-release (x86_64): pmhtutorial_1.5.tgz, r-oldrel (x86_64): pmhtutorial_1.5.tgz |
| Old sources: | pmhtutorial archive |
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These binaries (installable software) and packages are in development.
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