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Activity Number:
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351
- Variable Selection and Computationally Intensive Methods
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #311132
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Title:
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Integration of Two RShiny Applications into the Growclusters Package with an Example Implementation
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Author(s):
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Randall Powers* and Terrance Savitsky and Wendy Martinez
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Companies:
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U.S. Bureau of Labor Statistics and Bureau of Labor Statistics and Bureau of Labor Statistics
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Keywords:
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growclusters;
clustering;
R Shiny;
partition structure;
multivariate data
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Abstract:
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GrowClusters is an R package that estimates a clustering or partition structure for multivariate data. Estimation is performed under a penalized optimization derived from Bayesian non-parametric formulations. growclusters is an R package that estimates a clustering or partition structure for multivariate data. Estimation is performed under a penalized optimization derived from Bayesian non-parametric formulations. This is done either under a Dirichlet process (DP) mixing measure or a hierarchical DP (HDP) mixing measure in the limit of the global variance (to zero). The latter set-up allows for a collection of dependent, local partitions. This paper revisits the two R Shiny applications that were introduced in our 2019 paper and will focus on additional functionality added to both of them, as well as the integration of the R Shiny applications into the growclusters package. We will also present an example where the R Shiny applications are used to analyze the text from past papers presented at the United Nations Economic Commission for Europe workshops.
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Authors who are presenting talks have a * after their name.
