IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v053i08.html
   My bibliography  Save this article

profdpm: An R Package for MAP Estimation in a Class of Conjugate Product Partition Models

Author

Listed:
  • Shotwell, Matthew S.

Abstract

The profdpm package facilitates inference at the posterior mode for a class of product partition models. Dirichlet process mixtures are currently the only available class members. Several methods are implemented to search for the maximum posterior estimate of the data partition. This article discusses the relevant theory, the R and underlying C implementation, and examples of high level functionality.

Suggested Citation

  • Shotwell, Matthew S., 2013. "profdpm: An R Package for MAP Estimation in a Class of Conjugate Product Partition Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i08).
    • Handle: RePEc:jss:jstsof:v:053:i08
    DOI: http://hdl.handle.net/10.18637/jss.v053.i08
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v053i08/v53i08.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v053i08/profdpm_3.3.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v053i08/v53i08.R
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v053.i08?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
    2. Abel Rodríguez & David B. Dunson & Alan E. Gelfand, 2009. "Bayesian nonparametric functional data analysis through density estimation," Biometrika, Biometrika Trust, vol. 96(1), pages 149-162.
    3. Heard, Nicholas A. & Holmes, Christopher C. & Stephens, David A., 2006. "A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes: An Application of Bayesian Hierarchical Clustering of Curves," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 18-29, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    2. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).
    3. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    4. Angelini, Claudia & De Canditiis, Daniela & Pensky, Marianna, 2009. "Bayesian models for two-sample time-course microarray experiments," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1547-1565, March.
    5. Marc A. Scott & Kaushik Mohan & Jacques‐Antoine Gauthier, 2020. "Model‐based clustering and analysis of life history data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1231-1251, June.
    6. Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous–discrete covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 803-825, June.
    7. M. Battisti & F. Belloc & M. Del Gatto, 2017. "Technology-specific Production Functions," Working Paper CRENoS 201709, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    8. Lluís Bermúdez & Dimitris Karlis & Isabel Morillo, 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models," Risks, MDPI, vol. 8(1), pages 1-13, January.
    9. Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini & Simona Minotti, 2015. "Erratum to: The Generalized Linear Mixed Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 327-355, July.
    10. Frick, Hannah & Strobl, Carolin & Leisch, Friedrich & Zeileis, Achim, 2012. "Flexible Rasch Mixture Models with Package psychomix," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i07).
    11. Bruno Scarpa & David B. Dunson, 2014. "Enriched Stick-Breaking Processes for Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 647-660, June.
    12. Ando, Tomohiro & Bai, Jushan, 2021. "Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity," MPRA Paper 111431, University Library of Munich, Germany.
    13. Bruno Scarpa & David B. Dunson, 2009. "Bayesian Hierarchical Functional Data Analysis Via Contaminated Informative Priors," Biometrics, The International Biometric Society, vol. 65(3), pages 772-780, September.
    14. Antonello Maruotti & Pierfrancesco Alaimo Di Loro, 2023. "CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    15. Olga L Sarmiento & Andrés F Useche & Daniel A Rodriguez & Iryna Dronova & Oscar Guaje & Felipe Montes & Ivana Stankov & Maria Alejandra Wilches & Usama Bilal & Xize Wang & Luis A Guzmán & Fabian Peña , 2021. "Built environment profiles for Latin American urban settings: The SALURBAL study," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-25, October.
    16. Maik Dehnert & Josephine Schumann, 2022. "Uncovering the digitalization impact on consumer decision-making for checking accounts in banking," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1503-1528, September.
    17. Diani, Cecilia & Galimberti, Giuliano & Soffritti, Gabriele, 2022. "Multivariate cluster-weighted models based on seemingly unrelated linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    18. Prates, Marcos Oliveira & Lachos, Victor Hugo & Barbosa Cabral, Celso Rômulo, 2013. "mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i12).
    19. Papastamoulis, Panagiotis & Martin-Magniette, Marie-Laure & Maugis-Rabusseau, Cathy, 2016. "On the estimation of mixtures of Poisson regression models with large number of components," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 97-106.
    20. Miljkovic, Tatjana & Grün, Bettina, 2016. "Modeling loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 387-396.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:053:i08. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.