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mbrdr  

Model-Based Response Dimension Reduction
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
[Scholar Profile | Author Map]
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 14 Mar. 2025.
Previous versions and publish date:
1.0.8 (2021-08-17 10:40), 1.1.0 (2022-01-08 01:52)
Other packages that cited mbrdr R package
View mbrdr citation profile
Other R packages that mbrdr depends, imports, suggests or enhances
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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