IPCAPS-package: IPCAPS : Iterative Pruning to CApture Population Structure

Description Details Author(s) References See Also


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 (Intarapanich et al., 2009; Limpiti et al., 2011). The IPCAPS involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and Clustering EM estimation 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.


The R package IPCAPS requires the package KRIS.

Here is the list of functions in the R package IPCAPS:

Moreover, here is the list of example datasets in the R package IPCAPS:


Maintainer: Kridsadakorn Chaichoompu [email protected]



Intarapanich, A., Shaw, P.J., Assawamakin, A., Wangkumhang, P., Ngamphiw, C., Chaichoompu, K., Piriyapongsa, J., and Tongsima, S. (2009). Iterative pruning PCA improves resolution of highly structured populations. BMC Bioinformatics 10, 382.

Lebret, R., Iovleff, S., Langrognet, F., Biernacki, C., Celeux, G., and Govaert, G. (2015). Rmixmod: TheRPackage of the Model-Based Unsupervised, Supervised, and Semi-Supervised ClassificationMixmodLibrary. J. Stat. Softw. 67.

Limpiti, T., Intarapanich, A., Assawamakin, A., Shaw, P.J., Wangkumhang, P., Piriyapongsa, J., Ngamphiw, C., and Tongsima, S. (2011). Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure. BMC Bioinformatics 12, 255.

See Also

Useful links:

IPCAPS documentation built on May 2, 2019, 11:59 a.m.