Title: Differential Community Detection in Paired Biological Networks
Author: Raghvendra Mall [aut, cre], Khalid Kunji [ctb], Michele Ceccarelli [ctb]
Authors@R: c(person(given = "Raghvendra", family = "Mall",role = c("aut","cre"), email = "rmall@hbku.edu.qa"),
Description: A differential community detection (DCD) based approach to effectively locate differential sub-networks in paired scale-free biological networks, e.g. case vs control - Raghvendra Mall et al (2017) <doi:10.1145/3107411.3107418>.
Imports: Rdpack, data.table, igraph, ggplot2, ROCR, lsa, Matrix,
lattice, WGCNA, qlcMatrix, foreach, plyr, doParallel

Title: Clustering for Multivariate and Univariate Data Using
person("Sharma", "Parichit", email = "parishar@iu.edu", role = c("aut","cre", "ctb")),
person("Kurban", "Hasan", role = c("aut","ctb")),
person("Jenne", "Mark", role = c("aut","ctb")),
person("Dalkilic", "Mehmet", role = "aut"))
Description: Implements the Expectation Maximisation (EM) algorithm for clustering finite gaussian mixture models for
both multivariate and univariate datasets. The initialization is done by randomly selecting the samples from the
dataset as the mean of the Gaussian(s). Future versions will improve the parameter initialization and execution
on big datasets. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean, co-variance matrices
(multivariate data)/standard-deviation (for univariate datasets) and priors.
Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.
This work is partially supported by NCI Grant 1R01CA213466-01.
Author: Sharma Parichit [aut, cre, ctb],

dataset as the mean of the Gaussian(s). This version improves the parameter initialization on big datasets.
The algorithm returns a set of Gaussian parameters-posterior probabilities, mean, co-variance matrices

Description: Implements the Expectation Maximisation (EM)/(EM*) algorithm for clustering finite gaussian mixture models for
dataset as the mean of the Gaussian(s). This version implements the faster alternative EM* that avoids revisiting
data by leveraging the heap structure. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean, co-variance matrices