BayMDS: An R Package for Bayesian Multidimensional Scaling and Choice of Dimension

Man Suk Oh; Eun Kyung Lee

doi:10.1177/01466216221084219

BayMDS: An R Package for Bayesian Multidimensional Scaling and Choice of Dimension

Man Suk Oh, Eun Kyung Lee

Department of Statistics

Research output: Contribution to journal › Comment/debate

1 Scopus citations

Original language	English
Pages (from-to)	250-251
Number of pages	2
Journal	Applied Psychological Measurement
Volume	46
Issue number	3
DOIs	https://doi.org/10.1177/01466216221084219
State	Published - May 2022

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Basic Science Research Program (2019R1A2C1003086 and 2019R1A6A1A11051177 for M-S Oh; 2018R1A2B6001251 for E-K Lee) through the National Research Foundation of Korea (NRF) funded by the Korean government (MIST).

Keywords

Bayesian dimension selection
dissimilarity
multidimensional scaling
object configuration

Access to Document

10.1177/01466216221084219

Cite this

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title = "BayMDS: An R Package for Bayesian Multidimensional Scaling and Choice of Dimension",

keywords = "Bayesian dimension selection, dissimilarity, multidimensional scaling, object configuration",

author = "Oh, \{Man Suk\} and Lee, \{Eun Kyung\}",

note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Basic Science Research Program (2019R1A2C1003086 and 2019R1A6A1A11051177 for M-S Oh; 2018R1A2B6001251 for E-K Lee) through the National Research Foundation of Korea (NRF) funded by the Korean government (MIST). ",

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N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Basic Science Research Program (2019R1A2C1003086 and 2019R1A6A1A11051177 for M-S Oh; 2018R1A2B6001251 for E-K Lee) through the National Research Foundation of Korea (NRF) funded by the Korean government (MIST).

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