CRAN/E | mcb

mcb

Model Confidence Bounds

Installation

About

When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. doi:10.1111/biom.13024. Besides, 'flare' is needed only you apply the SQRT or LAD method ('mcb' totally has 8 methods). Although 'flare' has been archived by CRAN, you can still get it in and the latest version is useful for 'mcb'.

Key Metrics

Version 0.1.15
R ≥ 3.6.0
Published 2020-06-05 1393 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks mcb results

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Maintainer

Maintainer

Heming Den

dheming@ruc.edu.cn

Authors

Yang Li
Yichen Qin
Heming Deng

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

mcb archive

Depends

R ≥ 3.6.0

Imports

parallel
methods
leaps
lars
MASS
glmnet
ncvreg
smoothmest
ggplot2
reshape2

Suggests

flare
testthat