README.md
In therimalaya/envelope: Computing Envelope Estimators in Multivariate Linear Regression
Package: envlp
Type: Package
Title: Computing Envelope Estimators in Multivariate Linear Regression
Version: 1.7
Date: 2016-1-20
Author: Dennis Cook, Liliana Forzani, Zhihua Su
Maintainer: Dennis Cook dennis@stat.umn.edu, Liliana Forzani liliana.forzani@gmail.com, Zhihua Su zhihuasu@stat.ufl.edu
Description: This package provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. In the context of multivariate linear regression, this package implements response envelopes (env function), partial response envelopes (penv function) and envelopes in the predictor space (xenv function). For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at http://arxiv.org/abs/1509.03767. Optimization is based on a clockwise coordinate descent algorithm.
License: GPL-2
Packaged: 2016-01-25 20:22:26 UTC; zhihuasu
therimalaya/envelope documentation built on May 29, 2019, 1:38 p.m.
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Package: envlp
Type: Package
Title: Computing Envelope Estimators in Multivariate Linear Regression
Version: 1.7
Date: 2016-1-20
Author: Dennis Cook, Liliana Forzani, Zhihua Su
Maintainer: Dennis Cook dennis@stat.umn.edu, Liliana Forzani liliana.forzani@gmail.com, Zhihua Su zhihuasu@stat.ufl.edu
Description: This package provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. In the context of multivariate linear regression, this package implements response envelopes (env function), partial response envelopes (penv function) and envelopes in the predictor space (xenv function). For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at http://arxiv.org/abs/1509.03767. Optimization is based on a clockwise coordinate descent algorithm.
License: GPL-2
Packaged: 2016-01-25 20:22:26 UTC; zhihuasu
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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