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mbrdr
View on CRAN: Click
here
Download and install mbrdr package within the R console
Install from CRAN:
install.packages("mbrdr")
Install from Github:
library("remotes")
install_github("cran/mbrdr") Install by package version:
library("remotes")
install_version("mbrdr", "1.1.1") Attach the package and use:
library("mbrdr")
Maintained by
Jae Keun Yoo
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-08-17
Latest Update: 2022-01-24
Description:
Functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses. The response dimension reduction is theoretically founded in Yoo and Cook (2008) . Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) and Yoo (2019) . The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace.
How to cite:
Jae Keun Yoo (2021). mbrdr: Model-Based Response Dimension Reduction. R package version 1.1.1, https://cran.r-project.org/web/packages/mbrdr. Accessed 04 Jun. 2026.
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Complete documentation for mbrdr
Functions, R codes and Examples using
the mbrdr R package
Some associated functions: SIGMAS . choose.fx . matpower . mbrdr . mbrdr.x . mps .
Some associated R codes: mbrdr.R . Full mbrdr package functions and examples
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