reams: Resampling-Based Adaptive Model Selection

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Resampling methods for adaptive linear model selection. These can be thought of as extensions of the Akaike information criterion that account for searching among candidate models.

Author
Philip Reiss <phil.reiss@nyumc.org> and Lei Huang <huangracer@gmail.com>
Date of publication
2012-10-29 08:59:35
Maintainer
Tao Zhang <tao-zhang-1@uiowa.edu>
License
GPL (>= 2)
Version
0.1

View on CRAN

Man pages

bestmods
Find best submodels of a full linear model
cic
Covariance inflation criterion
cvic
Cross-validation information criterion
eic
Extended (bootstrap) information criterion
ic.min
AIC, corrected AIC and BIC for all-subsets linear regression
reams-package
Resampling-based adaptive model selection
scoremods
Score best subsets by information criteria
xy
Random generation of linear model matrix and outcomes

Files in this package

reams
reams/devel
reams/devel/plural.r
reams/devel/plursim.R
reams/devel/plursim.Rd
reams/NAMESPACE
reams/man
reams/man/ic.min.Rd
reams/man/scoremods.Rd
reams/man/cvic.Rd
reams/man/cic.Rd
reams/man/bestmods.Rd
reams/man/eic.Rd
reams/man/xy.Rd
reams/man/reams-package.Rd
reams/DESCRIPTION
reams/MD5
reams/R
reams/R/scoremods.R
reams/R/ic.min.R
reams/R/eic.R
reams/R/cic.R
reams/R/cvic.R
reams/R/xy.R
reams/R/bestmods.R