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jointMeanCov
View on CRAN: Click
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Download and install jointMeanCov package within the R console
Install from CRAN:
install.packages("jointMeanCov")
Install from Github:
library("remotes")
install_github("cran/jointMeanCov")
Install by package version:
library("remotes")
install_version("jointMeanCov", "0.1.0")
Attach the package and use:
library("jointMeanCov")
Maintained by
Michael Hornstein
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-05-04
Latest Update: 2019-05-04
Description:
Jointly estimates two-group means and covariances
for matrix-variate data and calculates test statistics.
This package implements the algorithms defined in
Hornstein, Fan, Shedden, and Zhou (2018)
.
How to cite:
Michael Hornstein (2019). jointMeanCov: Joint Mean and Covariance Estimation for Matrix-Variate Data. R package version 0.1.0, https://cran.r-project.org/web/packages/jointMeanCov. Accessed 07 Nov. 2024.
Previous versions and publish date:
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Complete documentation for jointMeanCov
Functions, R codes and Examples using
the jointMeanCov R package
Some associated functions: GLSMeans . GeminiB . GeminiBPath . centerDataGLSModelSelection . centerDataTwoGroupsByIndices . centerDataTwoGroupsByModelSelection . jointMeanCovGroupCen . jointMeanCovModSelCen . jointMeanCovStability . plot.jointMeanCov . summary.jointMeanCov . theoryRowpenUpperBound . theoryRowpenUpperBoundDiagA . twoGroupDesignMatrix .
Some associated R codes: GLSMeans.R . GeminiB.R . GeminiBPath.R . centerDataGLSModelSelection.R . centerDataTwoGroupsByIndices.R . centerDataTwoGroupsByModelSelection.R . jointMeanCovGroupCen.R . jointMeanCovModSelCen.R . jointMeanCovStability.R . plot.jointMeanCov.R . summary.jointMeanCov.R . theoryRowpenUpperBound.R . theoryRowpenUpperBoundDiagA.R . twoGroupDesignMatrix.R . Full jointMeanCov package functions and examples
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