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cap (version 1.0)

Covariate Assisted Principal (CAP) Regression for Covariance Matrix Outcomes

Description

Performs Covariate Assisted Principal (CAP) Regression for covariance matrix outcomes. The method identifies the optimal projection direction which maximizes the log-likelihood function of the log-linear heteroscedastic regression model in the projection space. See Zhao et al. (2018), Covariate Assisted Principal Regression for Covariance Matrix Outcomes, for details.

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Version

Install

install.packages('cap')

Monthly Downloads

39

Version

1.0

License

GPL (>= 2)

Maintainer

Yi Zhao

Last Published

September 30th, 2018

Functions in cap (1.0)

cap_beta

Inference of model coefficients
env.example

Simulated data
cap-package

Covariate Assisted Principal (CAP) Regression for Covariance Matrix Outcomes
capReg

Covariate Assisted Principal Regression for Covariance Matrix Outcomes