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Spatial clustering with hidden markov random field fitted via EM algorithm, details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.
Version: | 1.1 |
Depends: | mclust, parallel, ggplot2, Matrix, R (≥ 3.5) |
Imports: | Rcpp (≥ 1.0.6), SingleCellExperiment, purrr, BiocSingular, SummarizedExperiment, scater, scran, S4Vectors |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | knitr, rmarkdown |
Published: | 2021-10-08 |
DOI: | 10.32614/CRAN.package.SC.MEB |
Author: | Yi Yang [aut, cre], Xingjie Shi [aut], Jin Liu [aut] |
Maintainer: | Yi Yang <yygaosansiban at sina.com> |
License: | GPL-3 |
NeedsCompilation: | yes |
Materials: | README |
CRAN checks: | SC.MEB results |
Reference manual: | SC.MEB.pdf |
Vignettes: |
SC-MEB SC-MEB CRC |
Package source: | SC.MEB_1.1.tar.gz |
Windows binaries: | r-devel: SC.MEB_1.1.zip, r-release: SC.MEB_1.1.zip, r-oldrel: SC.MEB_1.1.zip |
macOS binaries: | r-release (arm64): SC.MEB_1.1.tgz, r-oldrel (arm64): SC.MEB_1.1.tgz, r-release (x86_64): SC.MEB_1.1.tgz, r-oldrel (x86_64): SC.MEB_1.1.tgz |
Old sources: | SC.MEB archive |
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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.
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