c(person("Danyu", "Lin", role = "aut"),
person("Yu", "Gu", role = "aut"),
person("Donglin", "Zeng", role = "aut"),
person("Shannon T.", "Holloway", role = c("aut", "cre"),
email = "shannon.t.holloway@gmail.com"))
Danyu Lin [aut],
Yu Gu [aut],
Donglin Zeng [aut],
Shannon T. Holloway [aut, cre]

Local Linear Hazard Estimation with Do-Validated and
Cross-Validated Bandwidths
M.L. Gamiz, E. Mammen, M.D. Martinez-Miranda and J.P. Nielsen
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.

Kernel Hazard Estimation with Best One-Sided and Double
One-Sided Cross-Validation
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.

Beta-binomial Mixture Model is used to infer the pattern from count data.
It can be used for clustering of RNA methylation sequencing data.

Sparse covariance estimation in heterogeneous samples
This package provides functions to estimate sparse
covariance in heterogenous samples

Dirichlet Process Mixtures (DPM) and related models using
Gaussian Graphical Models (GGM) for Sparse covariance
estimation in heterogeneous samples
This package provides functions to estimate sparse
covariance in heterogenous samples using Dirichlet Process
Mixtures (DPMs) with Gaussian Graphical Model (GGM) kernel
distributions.

Inference of Parameters of Normal Distributions from a Mixture
of Normals
c(
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"))
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.
Luis M. Avila [aut, cre],
Michael R. May [aut],
Jeff Ross-Ibarra [aut]