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Multiple-Inflated Negative Binomial Model

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Overview

About

Count data is prevalent and informative, with widespread application in many fields such as social psychology, personality, and public health. Classical statistical methods for the analysis of count outcomes are commonly variants of the log-linear model, including Poisson regression and Negative Binomial regression. However, a typical problem with count data modeling is inflation, in the sense that the counts are evidently accumulated on some integers. Such an inflation problem could distort the distribution of the observed counts, further bias estimation and increase error, making the classic methods infeasible. Traditional inflated value selection methods based on histogram inspection are easy to neglect true points and computationally expensive in addition. Therefore, we propose a multiple-inflated negative binomial model to handle count data modeling with multiple inflated values, achieving data-driven inflated value selection. The proposed approach provides simultaneous identification of important regression predictors on the target count response as well. More details about the proposed method are described in Li, Y., Wu, M., Wu, M., & Ma, S. (2023) arXiv:2309.15585.

Install

Health

CRAN checks
2NOTE11OK
Check messages
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Mingcong Wu <wumingcong@ruc.edu.cn>’

The Description field contains
  <arXiv:2309.15585>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Slowest check: 57 s · r-oldrel-windows-x86_64
Status changes
  • NOTE11 OK, 2 NOTENow
  • NOTE12 OK, 2 NOTE2026-03-10
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Documentation

In release 0.1.0
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
2
Examples that run
100%1 run in a plain check
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
0%

Downloads

2K
CRAN downloads in the past year
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23630 days
56290 days
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Also on67 r2u4 autocran

Dependencies

Declared dependencies
1 external dependency (excludes base and recommended)

Installing minb adds 1 package (1 with compiled code, 2.9 MB of source tarballs). Heaviest: pscl 2.9 MB.

Depends (1)
R >= 3.5.0
Imports (3)
MASSpsclstats
LinkingTo (0)
none
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (4)
Author, Maintainer since 0.1.0 (2023)
Author
Releases (1)
  • 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
  • 0.1.0Latest
    2023-10-01 · current release
  • R
    R 4.3.0 released · 2023-04-21

Package metadata

First published
2023-10-01
Total releases
1 / 3 yrs
License
GPL-3 OSI
Minimum R
≥ 3.5.0
Bundled data
7.8 KB / 1 file
Download size
14 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("minb")
Wu, M., Li, Y., Ma, S., & Wu, M. (2023). minb: Multiple-Inflated Negative Binomial Model (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.minb

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

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 minb version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-23. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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