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DynClust
View on CRAN: Click
here
Download and install DynClust package within the R console
Install from CRAN:
install.packages("DynClust")
Install from Github:
library("remotes")
install_github("cran/DynClust")
Install by package version:
library("remotes")
install_version("DynClust", "3.24")
Attach the package and use:
library("DynClust")
Maintained by
Yves Rozenholc
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-08-30
Latest Update: 2022-04-11
Description:
A two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
How to cite:
Yves Rozenholc (2012). DynClust: Denoising and Clustering for Dynamical Image Sequence (2D or 3D)+t. R package version 3.24, https://cran.r-project.org/web/packages/DynClust. Accessed 08 May. 2025.
Previous versions and publish date:
Other packages that cited DynClust R package
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Other R packages that DynClust depends,
imports, suggests or enhances
Complete documentation for DynClust
Functions, R codes and Examples using
the DynClust R package
Some associated functions: DynClust-package . GetClusteringResults . GetDenoisingResults . MultiTestH0 . RunClustering . RunDenoising . adu340_4small .
Some associated R codes: DynClust-package.R . GetClusteringResults.R . GetDenoisingResults.R . MultiTestH0.R . RunClustering.R . RunDenoising.R . adu340_4small.R . Full DynClust package functions and examples
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