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An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
| Version: | 1.1.8 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, utils, graphics, grDevices, MASS, Matrix, expm, KRIS, fpc, LPCM, apcluster, Rmixmod |
| Suggests: | testthat |
| Published: | 2021-01-25 |
| DOI: | 10.32614/CRAN.package.IPCAPS |
| Author: | Kridsadakorn Chaichoompu [aut, cre], Kristel Van Steen [aut], Fentaw Abegaz [aut], Sissades Tongsima [aut], Philip Shaw [aut], Anavaj Sakuntabhai [aut], Luisa Pereira [aut] |
| Maintainer: | Kridsadakorn Chaichoompu <kridsadakorn at biostatgen.org> |
| BugReports: | https://gitlab.com/kris.ccp/ipcaps/-/issues |
| License: | GPL-3 |
| URL: | https://gitlab.com/kris.ccp/ipcaps |
| NeedsCompilation: | no |
| Citation: | IPCAPS citation info |
| Materials: | README NEWS |
| CRAN checks: | IPCAPS results |
| Reference manual: | IPCAPS.pdf |
| Package source: | IPCAPS_1.1.8.tar.gz |
| Windows binaries: | r-devel: IPCAPS_1.1.8.zip, r-release: IPCAPS_1.1.8.zip, r-oldrel: IPCAPS_1.1.8.zip |
| macOS binaries: | r-release (arm64): IPCAPS_1.1.8.tgz, r-oldrel (arm64): IPCAPS_1.1.8.tgz, r-release (x86_64): IPCAPS_1.1.8.tgz, r-oldrel (x86_64): IPCAPS_1.1.8.tgz |
| Old sources: | IPCAPS 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.