DPpackage: Bayesian Semi- and Nonparametric Modeling in R

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dc.contributor.authorJara, Alejandro
dc.contributor.authorHanson, Timothy E.
dc.contributor.authorQuintana, Fernando A.
dc.contributor.authorMueller, Peter
dc.contributor.authorRosner, Gary L.
dc.date.accessioned2024-01-10T13:47:43Z
dc.date.available2024-01-10T13:47:43Z
dc.date.issued2011
dc.description.abstractData analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.
dc.description.funderFondecyt
dc.description.funderKUL-PUC bilateral (Belgium-Chile)
dc.description.funderIAP research network of the Belgian government (Belgian Science Policy)
dc.description.funderNIH
dc.description.funderNIH/NCI
dc.description.funderNATIONAL CANCER INSTITUTE
dc.format.extent30 páginas
dc.fuente.origenWOS
dc.identifier.issn1548-7660
dc.identifier.pubmedidMEDLINE:21796263
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79298
dc.identifier.wosidWOS:000289228600001
dc.information.autorucMatemática;Jara, A;S/I;127927
dc.information.autorucMatemática;Quintana, F;S/I;100343
dc.issue.numero5
dc.language.isoen
dc.nota.accesoSin adjunto
dc.pagina.final30
dc.pagina.inicio1
dc.publisherJOURNAL STATISTICAL SOFTWARE
dc.revistaJOURNAL OF STATISTICAL SOFTWARE
dc.rightsregistro bibliográfico
dc.subjectBayesian semiparametric analysis
dc.subjectrandom probability measures
dc.subjectrandom functions
dc.subjectMarkov chain Monte Carlo
dc.subjectR
dc.subjectPOLYA TREE DISTRIBUTIONS
dc.subjectDENSITY-ESTIMATION
dc.subjectINFERENCE
dc.subjectMIXTURES
dc.subjectPACKAGE
dc.subjectREGRESSION
dc.subjectFRAILTY
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleDPpackage: Bayesian Semi- and Nonparametric Modeling in R
dc.typeartículo
dc.volumen40
sipa.codpersvinculados127927
sipa.codpersvinculados100343
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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