Functions to perform inference via
simulation from the posterior distributions for Bayesian
nonparametric and semiparametric models. Although the name of
the package was motivated by the Dirichlet Process prior, the
package considers and will consider other priors on functional
spaces. So far, DPpackage includes models considering Dirichlet
Processes, Dependent Dirichlet Processes, Dependent Poisson-
Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
Trees, Linear Dependent Tailfree Processes, Mixtures of
Triangular distributions, Random Bernstein polynomials priors
and Dependent Bernstein Polynomials.  The package also includes
models considering Penalized B-Splines.
Includes semiparametric models for marginal and conditional
density estimation, ROC curve analysis, interval censored data,
binary regression models, generalized linear mixed models, IRT
type models, and generalized additive models. Also
contains functions to compute Pseudo-Bayes factors for model
comparison, and to elicitate the precision parameter of the
Dirichlet Process. To maximize computational efficiency, the
actual sampling for each model is done in compiled FORTRAN. The
functions return objects which can be subsequently analyzed
with functions provided in the 'coda' package.
Orphaned and corrected on 2017-09-15 as errors were not
corrected despite repeated reminders.

person("Shaoyang", "Ning", email = "shaoyangning@fas.harvard.edu", role = c("aut", "cre"))
Contains functions to perform copula estimation
by the non-parametric Bayesian method,
Dirichlet-based Polya Tree. See Ning (2018) <doi:10.1080/00949655.2017.1421194>.

Semiparametric Bayesian procedure for selecting a subset
containing the weakest species with an acceptably high
probability.
The purpose of the functions implemented in this package
is to find a subset containing the weakest species with some
acceptable certainty. Suppose we have sampled some quality
measures T_{k,j} (>=0) from K species (k=1,...,K, j=1,...,m_k=
sample size of kth species), and wish to find the species with
the smallest alpha quantile (say, 5th percentile) of T (namely
the weakest species). Due to the small sample size (m_k) or
large number of species to be considered (K), we might not be
able to select a single species as a weakest one with
acceptably large certainty. Instead of selecting a single
species with a low certainty, we propose to select a subset of
species which contains the weakest species with predetermined
certainty.