Coarsened Mixtures of Hierarchical Skew Kernels
c(person("S.", "Gorsky", role = c("aut", "cre"),
email = "sgorsky@umass.edu"),
person("C.", "Chan", role = "ctb",
email = "cliburn.chan@duke.edu"),
person("L.", "Ma", role = "ctb",
email = "li.ma@duke.edu"))
Bayesian fit of a Dirichlet Process Mixture with hierarchical multivariate skew normal kernels and coarsened posteriors. For more information, see Gorsky, Chan and Ma (2020) <arXiv:2001.06451>.
S. Gorsky [aut, cre],
C. Chan [ctb],
L. Ma [ctb]

Rcpp (>= 0.12.18), dplyr, ggplot2, stringr, coda, tidyr,
rlang, methods

Albert Chen [aut, cre], Timothy E Sweeney [aut], Olivier Gevaert [ths]
c(person("Albert", "Chen", role = c("aut", "cre"),
email = "acc2015@stanford.edu"),
person(c("Timothy", "E"), 	"Sweeney", role = "aut",
email = "tes17@stanford.edu"),
person("Olivier", "Gevaert", role = "ths",
email = "ogevaert@stanford.edu"))
Facilitates optimal clustering of a data set. Provides a framework to run a wide range of clustering algorithms to determine the optimal number (k) of clusters in the data. Then analyzes the cluster assignments from each clustering algorithm to identify samples that repeatedly classify to the same group. We call these 'core clusters', providing a basis for later class discovery.

RUnit, NMF, ConsensusClusterPlus, rgl, kohonen
methods, grDevices, graphics, stats, utils

Fit a CMP regression model to count data. The code
provides functions for model estimation, dispersion testing,
and diagnostics.

Kimberly Sellers <kfs7@georgetown.edu>, Thomas Lotze
<thomas.lotze@thomaslotze.com>

Kimberly Sellers <kfs7@georgetown.edu>, Thomas Lotze <thomas.lotze@thomaslotze.com>
Fit a CMP regression model to count data. The code provides functions for model estimation, dispersion testing, and diagnostics.

Kimberly Sellers <kfs7@georgetown.edu>
Thomas Lotze <thomas.lotze@thomaslotze.com>
Andrew Raim <andrew.raim@gmail.com>
Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models
to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The
package provides functions for model estimation, dispersion testing, and
diagnostics. Zero-inflated CMP regression (Raim & Sellers, 2016)
<doi:10.1016/j.csda.2016.01.007> is also supported.