Package: DPpackage
Version: 1.1-4
Date: 2012-01-02
Title: Bayesian nonparametric modeling in R
Author@R: c(person("Alejandro", "Jara", email = "atjara@uc.cl"),
        person("Timothy", "Hanson", role = "ctb", email =
        "hansont@stat.sc.edu"), person("Fernando", "Quintana", role =
        "ctb", email = "quintana@mat.puc.cl"), person("Peter",
        "Mueller", role = "ctb", email = "pmueller@math.utexas.edu"),
        person("Gary", "Rosner", role = "ctb", email =
        "grosner@jhmi.edu"))
Author: Alejandro Jara <atjara@uc.cl> with contributions from Timothy
        Hanson <hanson@biostat.umn.edu>, Fernando A. Quintana
        <quintana@mat.puc.cl>, Peter Mueller <pmueller@math.utexas.edu>,
        and Gary L. Rosner <grosner@jhmi.edu>.
Maintainer: Alejandro Jara <atjara@uc.cl>
Depends: R (>= 2.10), MASS, nlme, survival, splines
Description: This package contains 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. Currently the package 
        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. The package 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.
License: GPL (>= 2)
URL: http://www.mat.puc.cl/~ajara
Packaged: 2012-01-03 06:54:40 UTC; root
Repository: CRAN
Date/Publication: 2012-01-03 08:37:40
