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jointMeanCov (version 0.1.0)

Joint Mean and Covariance Estimation for Matrix-Variate Data

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) .

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Version

Install

install.packages('jointMeanCov')

Monthly Downloads

171

Version

0.1.0

License

GPL-2

Maintainer

Michael Hornstein

Last Published

May 4th, 2019

Functions in jointMeanCov (0.1.0)

GeminiBPath

Estimate Row-Row Covariance Using Gemini for a Sequence of Penalties
GeminiB

Estimate Row-Row Covariance Structure Using Gemini
jointMeanCovStability

Estimate Mean and Correlation Structure Using Stability Selection
plot.jointMeanCov

Quantile Plot of Test Statistics
summary.jointMeanCov

Summary of Test Statistics
theoryRowpenUpperBound

Penalty Parameter for Covariance Estimation Based on Theory
GLSMeans

Generalized Least Squares
centerDataTwoGroupsByIndices

Center Each Column by Subtracting Group Means
centerDataTwoGroupsByModelSelection

Center Each Column Based on Model Selection
centerDataGLSModelSelection

Center Each Column By Subtracting Group or Global GLS Mean
theoryRowpenUpperBoundDiagA

Penalty Parameter for Covariance Estimation Based on Theory
jointMeanCovGroupCen

Estimate Mean and Row-Row Correlation Matrix Using Group Centering
jointMeanCovModSelCen

Estimate Mean and Row-Row Correlation Matrix Using Model Selection
twoGroupDesignMatrix

Design Matrix for Two-Group Mean Estimation