neuroconductor/cap: Covariate Assisted Principal (CAP) Regression for Covariance Matrix Outcomes
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, <doi:10.1101/425033> for details.
Getting started
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Package details |
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| Author | Yi Zhao <zhaoyi1026@gmail.com>, Bingkai Wang <bwang51@jhmi.edu>, Stewart Mostofsky <mostofsky@kennedykrieger.org>, Brian Caffo <bcaffo@gmail.com>, Xi Luo <xi.rossi.luo@gmail.com> |
| Maintainer | Yi Zhao <zhaoyi1026@gmail.com> |
| License | GPL (>=2) |
| Version | 1.0 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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