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bayesImageS
View on CRAN: Click
here
Download and install bayesImageS package within the R console
Install from CRAN:
install.packages("bayesImageS")
Install from Github:
library("remotes")
install_github("cran/bayesImageS")
Install by package version:
library("remotes")
install_version("bayesImageS", "0.6-1")
Attach the package and use:
library("bayesImageS")
Maintained by
Matt Moores
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-11-03
Latest Update: 2021-04-11
Description:
Various algorithms for segmentation of 2D and 3D images, such
as computed tomography and satellite remote sensing. This package implements
Bayesian image analysis using the hidden Potts model with external field
prior of Moores et al. (2015) .
Latent labels are sampled using chequerboard updating or Swendsen-Wang.
Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),
and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to
for an overview and also to
and for further details of specific algorithms.
How to cite:
Matt Moores (2016). bayesImageS: Bayesian Methods for Image Segmentation using a Potts Model. R package version 0.6-1, https://cran.r-project.org/web/packages/bayesImageS. Accessed 22 Dec. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for bayesImageS
Functions, R codes and Examples using
the bayesImageS R package
Some associated functions: bayesImageS . exactPotts . getBlocks . getEdges . getNeighbors . gibbsGMM . gibbsNorm . gibbsPotts . initSedki . mcmcPotts . mcmcPottsNoData . res . res2 . res3 . res4 . res5 . smcPotts . sufficientStat . swNoData . synth . testResample .
Some associated R codes: bayesImageS.R . data.R . getBlocks.R . getEdges.R . getNeighbors.R . mcmcPotts.R . smcPotts.R . Full bayesImageS package functions and examples
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