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PAWL (version 0.5)

Implementation of the PAWL algorithm

Description

Implementation of the Parallel Adaptive Wang-Landau algorithm. Also implemented for comparison: parallel adaptive Metropolis-Hastings,SMC sampler.

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Version

Install

install.packages('PAWL')

Monthly Downloads

9

Version

0.5

License

GPL (>= 2)

Maintainer

Pierre Jacob

Last Published

November 15th, 2012

Functions in PAWL (0.5)

tuningparameters

MCMC Tuning Parameters
PlotFH

Plot of the Flat Histogram occurrences
PlotHist

Plot a histogram of one component of the chains
PlotNbins

Plot of the increase of the number of bins along the iterations
PAWL-package

PARALLEL ADAPTIVE WANG-LANDAU
createAdaptiveRandomWalkProposal

Adaptive Random Walk proposal distribution for MCMC algorithms
binning

Class "binning"
createTrimodalTarget

Trimodal target distribution
normalizeweight

Normalize weights
ConvertResults

Convert Results
smcparameters

SMC Tuning Parameters
proposal

Class "proposal"
PlotAllVar

Trace plot of all the variables
preexplorationAMH

Pre exploration Adapative Metropolis-Hastings
target

Class: target distribution
IceFloe

Image of ice floes
Pollution

Pollution Data
adaptiveMH

Adaptive Metropolis-Hastings
pawl

Parallel Adaptive Wang-Landau
PlotComp1vsComp2

Plot one component versus another in a scatter plot
PlotLogTheta

Plot of the log theta penalties
createMixtureTarget

Mixture target distribution
smc

Sequential Monte Carlo
PlotHistBin

Plot a histogram of the binning coordinate
getFrequencies

Observed Frequencies in each bin.
PlotDensComp1vsComp2

Plot one component versus another in a density plot