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Model Confidence Bounds

0packages depend
2.3Kdownloads / year
25.2%test coverage
9/13flavors OK

Overview

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 Although 'flare' has been archived by CRAN, you can still get it in https://CRAN.R-project.org/package=flare and the latest version is useful for 'mcb'.

Install

Health

CRAN checks
4NOTE9OK
Check messages
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Heming Deng<dheming@ruc.edu.cn>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Yang",
                      family = "Li",
                      role = "aut"),
               person(given = "Yichen",
                      family = "Qin",
                      role = "aut"),
               person(given = "Heming",
                      family = "Deng",
                      role = c("aut", "cre"),
                      email = "dheming@ruc.edu.cn"))
as necessary.
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespaces in Imports field not imported from:
  ‘MASS’ ‘lars’ ‘methods’ ‘parallel’ ‘smoothmest’
All declared Imports should be used.

CRAN check page ↗

Slowest check: 2.8 min · r-oldrel-windows-x86_64
Status changes
  • NOTE9 OK, 4 NOTENow
  • NOTE10 OK, 4 NOTE2026-03-10
Advisories

none known

Documentation

In release 0.1.15
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
3
Examples that run
100%2 run in a plain check
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
100%

Downloads

2.3K
CRAN downloads in the past year
Rank #17,944 · ~6/day · ~195/mo
Daily download trend is not available in this view yet.
27030 days
72490 days
2.3K1 year
Compare downloads with other packages →
Also on65 r2u2 autocran

Dependencies

Declared dependencies
9 external dependencies (excludes base and recommended)

Installing mcb adds 32 packages (17 with compiled code, 33 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, glmnet 2.7 MB.

Depends (1)
R >= 3.6.0
Imports (10)
LinkingTo (0)
none
Suggests (2)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (from the free-text Author field) (3)

Maintainer since 0.1.9 (2020)

Releases (2)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • unarchivedReturned to CRAN
    2020-06-05
  • 0.1.15Latest
    record
    2020-06-05 · current release · diff ↗
  • archivedRemoved from CRAN
    2020-05-14
    it requires archived package 'pancor'
  • 0.1.9
    record
    2020-05-02
  • R
    R 4.0.0 released · 2020-04-24

Package metadata

First published
2020-05-02
Total releases
2 / 6 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 3.6.0
Bundled data
6.6 KB / 1 file
Download size
15 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("mcb")

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for mcb version 0.1.15 [Data set]. HJJB, LLC. Data release v2026-10-24. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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