Imports: Rcpp (>= 0.12.18), dplyr, ggplot2, stringr, coda, tidyr,

Title: Robust Selection of Cluster Number K
Author: Albert Chen [aut, cre], Timothy E Sweeney [aut], Olivier Gevaert [ths]
Authors@R: c(person("Albert", "Chen", role = c("aut", "cre"),
person(c("Timothy", "E"), 	"Sweeney", role = "aut",
person("Olivier", "Gevaert", role = "ths",
Description: 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.
Packaged: 2015-02-11 18:01:27 UTC; stanfordinoxford

Depends: R (>= 2.10), cluster, clValid, fpc
Suggests: RUnit, NMF, ConsensusClusterPlus, rgl, kohonen
Imports: methods, grDevices, graphics, stats, utils

Title: Conway-Maxwell Poisson (COM-Poisson) Regression
Description: Fit a CMP regression model to count data. The code
provides functions for model estimation, dispersion testing,

Author: Kimberly Sellers <kfs7@georgetown.edu>, Thomas Lotze

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

Description: 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)

diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016)

Title: Copy Number Profile Curve-Based Association Test
Author: Amanda Brucker, Shannon T. Holloway, Jung-Ying Tzeng
Description: Implements a kernel-based association test for copy number
in a certain genomic region (e.g., gene set, chromosome, or genome) that is
robust to the within-locus and across-locus etiological heterogeneity, and
bypass the need to define a "locus" unit for CNVs.
Collate: 'commonAUC.R' 'segLength.R' 'popFunc.R' 'uniqueCombinations.R'
'createDF.R' 'deldup.R' 'CAUCkernel.R' 'cnvData.R' 'compFunc.R'
'dataChecks.R' 'test.qt.R' 'test.bt.R' 'pv_func.R' 'concur.R'