person("Shannon T.", "Holloway", role = c("aut", "cre"),

Title: Local Linear Hazard Estimation with Do-Validated and
Author: M.L. Gamiz, E. Mammen, M.D. Martinez-Miranda and J.P. Nielsen
Description: Local linear estimator for the univariate hazard (hazard rate) and bandwidth parameter selection using the do-validation method and the standard least squares cross-validation method.

Title: Kernel Hazard Estimation with Best One-Sided and Double
Description: Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.

Title: Dirichlet Process Beta-Binomial Mixture
Imports: tmvtnorm, VGAM, gplots, CEoptim
Description: Beta-binomial Mixture Model is used to infer the pattern from count data.
It can be used for clustering of RNA methylation sequencing data.

Title: Sparse covariance estimation in heterogeneous samples
Author: Alex Lenkoski, Andreas Neudecker
Description: This package provides functions to estimate sparse

Title: Dirichlet Process Mixtures (DPM) and related models using
Gaussian Graphical Models (GGM) for Sparse covariance
covariance in heterogenous samples using Dirichlet Process
Mixtures (DPMs) with Gaussian Graphical Model (GGM) kernel

Title: Inference of Parameters of Normal Distributions from a Mixture
person("Luis M.", "Avila", email = "lmavila@gmail.com", role = c("aut","cre")),
person("Michael R.", "May", email = "mikeryanmay@ucdavis.edu", role = "aut"),
person("Jeff", "Ross-Ibarra", email = "rossibarra@ucdavis.edu", role = "aut"))
Description: This MCMC method takes a data numeric vector (Y) and assigns the elements of Y
to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred.
Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
Depends: methods, Rcpp (>= 0.12.4), coda
Maintainer: Luis M. Avila <lmavila@gmail.com>