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pmhtutorial: Minimal Working Examples for Particle Metropolis-Hastings

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
DOI: 10.32614/CRAN.package.pmhtutorial
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

Documentation:

Reference manual: pmhtutorial.pdf

Downloads:

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

Linking:

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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.