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Programs
Linked to this page are programs for the following papers.
See the publication page for
citation details and related papers.
- PPMx: (replaces old CLUSTERx )
program
- SSM:
program
- Drug screening:
program
- NP Bayesian inference for differential gene expression:
program
- Dirichlet process mixture (MDP) of normals:
program
- Hierarchical DP mixture of normals:
program
- ANOVADDP univariate:
program
- Hierarchical DP mixture for mixed effects models:
program
- ANOVADDP:
program
- Protein biomarker discovery:
program
The last three programs are specialized software for examples from
various papers.
Random partitions with regression on covariates: R package "PPMx"
Applicability: general .
The program is suitable for a wide range of applications.
it implements the method proposed in
Mueller, Quintana and Rosner (2008)
(pdf)
The program is implemented as an
R library.
The algorithm proposed in the paper can be carried out
by calling the appropriate functions in the library.
Meaningful defaults are supplied for all tuning parameters.
The functions should be straightforward to use for any user
who is familiar with R.
Mueller, P., Quintana, F. and Rosner, G. (2008)
"Bayesian Clustering with Regression"
(pdf)
Screening Designs for Drug Development:
R package 'seqdesphII'
Applicability: general .
The program is suitable for a wide range of applications.
it implements the method proposed in the Rossell, Mueller and Rosner (2005)
(pdf)
The program is implemented as an
R library.
The algorithm proposed in the paper can be carried out
by calling the appropriate functions in the library.
Meaningful defaults are supplied for all tuning parameters.
The functions should be straightforward to use for any user
who is familiar with R.
A Bayesian Mixture Model for Differential Gene Expression (R and C)
Applicability: general .
The program is suitable for a wide range of applications. It
implements the method proposed in the
paper by
Do, Mueller and Tang (2002).
- Documentation:
- Download:
Download the program from
here.
DP Mixture (MDP) of normals: R package 'mdp'
Applicability: general .
The program is suitable for a wide range of applications. It
implements a popular model for random probability measures (RPM). The
RPM would typically be part of a larger probability model, using the
RPM, for example, as random effects distribution in a mixed effects model,
as exposure model in a retrospective sampling model, as residual distribution
in a regression problem, as event time distribution in a survival model, etc.
Description:
Implements MCMC for a multivariate DP mixture of normal
model for a random probabilty measure.
For a discussion of the model see, for example:
MacEachern, S.N. and Mueller, P. (1998).
``Estimating Mixture of Dirichlet Process Models,''
Journal of Computational and Graphical Statistics , 7,
223--239.
(pdf)
Hierarchical DP Mixture: R package 'hdpmn'
Applicability: general .
The program is suitable for a wide range of applications. It
implements a relatively simple model for a set of dependent random probability
measures (RPMs).
The RPMs would typically be part of a larger probability model, using the
RPMs, for example, as random effects distributions in a mixed effects model
across different subpopulations,
as sampling models for different subpopulations in a hierarchical model,
etc.
Description:
Implements MCMC for a set of dependent random probability measures
H[j] with a hierarchical DP prior. The hierarchy is constructed
by assuming that each H[j] arises as a mixture of a common
measure F[0] and a idiosyncratic measure F[j] specific to the submodel.
For details see
Mueller, P., Quintana, F. and Rosner, G. (2004).
``Hierarchical Meta-Analysis over Related Non-parametric Bayesian
Models.''
Journal of the Royal Statistical Society, Series B , 66,
735--749.
(pdf)
ANOVA DDP (univariate): R package 'ddpanova'
Applicability: general .
The program implements the ANOVA DDP model for
univariate outcomes.
The program is suitable for a wide range of applications.
It implements a model for dependent random probability measures (RPM). The
nature of the dependence is an ANOVA structure.
The RPMs might be sampling models for event times in a survival analysis
across related (but not exchangeable) subpopulations.
Or the RPMs might be used as random effects distributions in a
mixed effects model, etc.
Description:
The R package implements the ANOVA DDP model as described
in Mueller et al (2005), but without the top level
longitudinal profile model.
Now with new function for (censored) event time data.
De Iorio, M., Mueller, P., Rosner, G., and Maceachern, S. (2004).
``An ANOVA Model for Dependent Random Measures,''
JASA, 99(465), 205--215.
(pdf)
Hierarchical DP mixture for mixed effects models:
R package 'hdpm'
Applicability: specialized .
The program is specific to the example in the paper
Mueller et al. (2004) for semiparametric inference
for repeated measurements data across related subpopulations.
The specific model for the longitudinal profile is hard-coded.
A user who wishes to adapt the program to another application, with
a different parametrization of the longitudinal would need to change
some of the underlying C functions. The program includes documentation on
how to do this. But it requires some expertise.
Description:
This package implements a semi-parametric mixed efffects model for
related studies. The random effects distribution is a hierarchical
DP mixture as in hdpmn .
An additional nonlinear regression defines a sampling model for the
repeated measurements over time, conditional on subject-specific
random effects.
The program implements the specific nonlinear regression model used in
the paper. For alternative models, either change the function definitions
in the source code (difficult), or use hdpmn
with ad-hoc (e.g., m.l.e.) point estimates for the random effects vectors
(easy).
Mueller, P., Quintana, F. and Rosner, G. (2004).
``Hierarchical Meta-Analysis over Related Non-parametric Bayesian
Models.''
Journal of the Royal Statistical Society, Series B , 66,
735--749.
(pdf)
ANOVA DDP (multivariate): R package 'anovaddp'
Applicability: specialized .
The program is specific to the example in the paper
Mueller et al. (2005) for dependent random probability measures,
where dependence is defined in an ANOVA fashion (ANOVA DDP).
The program is specialized because it implements the ANOVA DDP
in the context of a repeated measurements model with related subpopulations.
The ANOVA DDP defines the random effects distributions across subpopulations.
The program is specialized because it includes a specific sampling model for
the longitudinal measurements (conditional on random effects).
It is difficult to adapt the program to general use.
We plan to develop a simplified version of the program to only implement
the ANOVA DDP, without the repeated measurements model, that would
be useful for other users.
Description:
The R package implements the ANOVA DDP model for
Mueller, P., Rosner, G., De Iorio, M., and MacEachern, S. (2005).
``A Nonparametric Bayesian Model for Inference
in Related Studies.''
Applied Statistics , 54 (3), 611-626.
SSM: R macros "SSM"
Applicability: illustration example only .
These R macros show inference for a proposed new class of models
SSM(G0,p) .
The implementation serves as illustration example only. It is not
suitable for actual data analysis.
Lee, Quintana, Mueller and Trippa (2008)
"Defining Predictive Probability Functions for Species Sampling Models"
(pdf)