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pmhtutorial
View on CRAN: Click
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Download and install pmhtutorial package within the R console
Install from CRAN:
install.packages("pmhtutorial")
Install from Github:
library("remotes")
install_github("cran/pmhtutorial")
Install by package version:
library("remotes")
install_version("pmhtutorial", "1.5")
Attach the package and use:
library("pmhtutorial")
Maintained by
Johan Dahlin
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-01-19
Latest Update: 2019-03-22
Description:
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.
How to cite:
Johan Dahlin (2016). pmhtutorial: Minimal Working Examples for Particle Metropolis-Hastings. R package version 1.5, https://cran.r-project.org/web/packages/pmhtutorial
Previous versions and publish date:
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Functions, R codes and Examples using
the pmhtutorial R package
Some associated functions: example1_lgss . example2_lgss . example3_sv . example4_sv . example5_sv . generateData . kalmanFilter . makePlotsParticleMetropolisHastingsSVModel . particleFilter . particleFilterSVmodel . particleMetropolisHastings . particleMetropolisHastingsSVmodel . particleMetropolisHastingsSVmodelReparameterised .
Some associated R codes: dataGeneration.R . example1-lgss.R . example2-lgss.R . example3-sv.R . example4-sv.R . example5-sv.R . parameterEstimation.R . plotting.R . stateEstimation.R . Full pmhtutorial package functions and examples
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