Description: 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.

Description: 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.

Title: Partition-Assisted Clustering and Multiple Alignments of
Maintainer: Ye Henry Li <ywli@stanford.edu>
Description: Implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.
Imports: Rcpp (>= 0.12.2),igraph,parmigene,infotheo,dplyr, Rtsne,

Description: Implements Partition-Assisted Clustering and Multiple Alignments of Networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set.

Maintainer: Ye Henry Li <hlowl2@gmail.com>

Description: A function for clustering online datasets. The number
of cells is data-driven which need not to be chosen in advance by the user.
The method is introduced and fully described in Le Li, Benjamin Guedj and Sebastien Loustau (2016), "PAC-Bayesian Online Clustering" (arXiv preprint: <https://arxiv.org/abs/1602.00522>).