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IPCAPS
View on CRAN: Click
here
Download and install IPCAPS package within the R console
Install from CRAN:
install.packages("IPCAPS")
Install from Github:
library("remotes")
install_github("cran/IPCAPS") Install by package version:
library("remotes")
install_version("IPCAPS", "1.1.8") Attach the package and use:
library("IPCAPS")
Maintained by
Kridsadakorn Chaichoompu
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-06-14
Latest Update:
Description:
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) . 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) . In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
How to cite:
Kridsadakorn Chaichoompu (2018). IPCAPS: Iterative Pruning to Capture Population Structure. R package version 1.1.8, https://cran.r-project.org/web/packages/IPCAPS. Accessed 11 Mar. 2026.
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imports, suggests or enhances
Complete documentation for IPCAPS
Functions, R codes and Examples using
the IPCAPS R package
Some associated functions: IPCAPS-package . PC . cal.eigen.fit . check.stopping . clustering.mode . clustering . diff.eigen.fit . diff.xy . do.glm . export.groups . get.node.info . ipcaps . label . output.template . pasre.categorical.data . postprocess . preprocess . process.each.node . raw.data . replace.missing . save.eigenplots.html . save.html . save.plots.cluster.html . save.plots.label.html . save.plots . top.discriminator .
Some associated R codes: check.stopping.R . clustering.R . clustering.mode.R . data.R . export.groups.R . get.node.info.R . ipcaps-package.R . ipcaps.R . output.template.R . parallelization.R . postprocess.R . preprocess.R . process.each.node.R . save.eigenplots.html.R . save.html.R . save.plots.R . save.plots.cluster.html.R . save.plots.label.html.R . top.discriminator.R . Full IPCAPS package functions and examples
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