Provides utilities for processing of Oxy-Bisulfite microarray data
(e.g. via the Illumina Infinium platform, <http://www.illumina.com>)
with tandem arrays, one using conventional
bisulfite conversion, the other using oxy-bisulfite conversion.

Posterior Predictive Checks of Coalescent Models
genealogicalSorting (>= 0.92), phybase (>= 1.3.1), Rmpi (>=
0.6-5), xtermStyle (>= 2.2-4)
P2C2M is an R package to conduct posterior predictive checks of coalescent models using gene and species trees generated by BEAST and *BEAST, respectively. The functionality of P2C2M can be extended via two third-party R packages that are available from the author websites only: genealogicalSorting (http://www.genealogicalsorting.org) and phybase (http://odyssey.bioinformatics.uga.edu/~lliu/phybase/). To use these optional packages, installation of the Python libraries NumPy (>= 1.9.0) and DendroPy (= 3.12.0) is necessary.

P2C2M is an R package to conduct posterior predictive checks of coalescent models using gene and species trees generated by BEAST or *BEAST. The functionality of P2C2M can be extended via two third-party R packages that are available from the author websites only: genealogicalSorting (http://www.genealogicalsorting.org) and phybase (http://odyssey.bioinformatics.uga.edu/~lliu/phybase/). To use these optional packages, the installation of the Python libraries NumPy (>= 1.9.0) and DendroPy (= 3.12.0) is required.

Conducts posterior predictive checks of coalescent models using gene and species trees generated by 'BEAST' or '*BEAST'. The functionality of P2C2M can be extended via two third-party R packages that are available from the author websites: 'genealogicalSorting' and 'phybase'. To use these optional packages, the installation of the Python libraries 'NumPy' (>= 1.9.0) and 'DendroPy' (= 3.12.0) is required.

Implements Partition-Assisted Clustering, which utilizes a collection of partition based nonparametric density estimation techniques to improve the robustness and accuracy of downstream clustering.
The package also provides functions for effectively visualizing the clustering results.
It is particularly useful for finding and visualizing subpopulations in single-cell data analysis.