[R] [R-pkgs] DPpackage - New version

From: Alejandro Jara Vallejos <Alejandro.JaraVallejos_at_med.kuleuven.be>
Date: Tue, 29 May 2007 18:10:27 +0200

Dear List:

I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status.

The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So far, the package includes models based on Dirichlet processes, Dirichlet process mixtures of normals, Polya trees, and Random Bernstein polynomials. A list of current functions is given next:

  1. Density estimation: DPdensity (using DPM of normals), PTdensity
    (using Mixtures of Polya Trees), and BDPdensity (using
    Bernstein-Dirichlet prior). The first two functions allow uni- and multi-variate analysis.
  2. Nonparametric random effects distributions in mixed effects models:

    2.1) DPlmm and DPMlmm, using a DP/MDP and DPM of normals prior, respectively, for the linear mixed effects model.

    2.2) DPglmm and DPMglmm, using a DP/MDP and DPM of normals prior, respectively, for generalized linear mixed effects models, respectively. The sampling(link) considered by these functions are binomial(logit,probit), poisson(log) and gamma(log).

    2.3) DPolmm and DPMolmm, using a DP/MDP and DPM of normals prior, respectively, for the probit-ordinal mixed effects models.

    2.4) DPrasch and FPTrasch, using a DP/MDP and finite PT/MPT
(mixture of Polya Trees) prior for the Rasch model with binary
sampling distribution, respectively.

    2.5) DPraschpoisson and FPTraschpoisson. The same as before (2.4) but with a Poisson sampling.

    2.6) DPmeta and DPMmeta for the random (mixed) effects meta-analysis models, using a DP/MDP and DPM of normals prior, respectively.

3) Binary regression with nonparametric link:

    3.1) CSDPbinary, using Newton, Czado and Chappell (1996)'s centrally standardized DP prior.

    3.2) DPbinary, using the regular DP prior for the inverse of the link function.

    3.3) FPTbinary, using a finite PT prior for the inverse of the link function.

4) AFT model for interval-censored data:

    4.1) DPsurvint, using a MDP prior for the baseline distribution.

5) ROC curve estimation:

    5.1) DProc, using DPM of normals.

6) Linear model with a nonparametric for the error distribution:

    6.1) PTlm, using MPT.

7) DP prior elicitation:

    7.1) DPelicit, using the exact and approximated formulas for the mean and variance of the number of clusters given the total mass parameter and the number of subjects.

Tim Hanson and Fernando Quintana have made contributions to the current version. I would also like to thank George Karabatsos for his input to the current status of the package and Peter Mueller for actively promoting the package.

Various other improvements have been motivated by questions asked by many people around the world. I would like to thank all of them too.

I welcome anyone who sends comments, suggestions, remarks, and particularly those who find bugs or mistakes in any part of the package or its documentation. DPpackage is an open source program for Bayesian nonparametric developments. All contributions are welcome.

Best regards,


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