countts: Thomson Sampling for Zero-Inflated Count Outcomes

A specialized tool is designed for assessing contextual bandit algorithms, particularly those aimed at handling overdispersed and zero-inflated count data. It offers a simulated testing environment that includes various models like Poisson, Overdispersed Poisson, Zero-inflated Poisson, and Zero-inflated Overdispersed Poisson. The package is capable of executing five specific algorithms: Linear Thompson sampling with log transformation on the outcome, Thompson sampling Poisson, Thompson sampling Negative Binomial, Thompson sampling Zero-inflated Poisson, and Thompson sampling Zero-inflated Negative Binomial. Additionally, it can generate regret plots to evaluate the performance of contextual bandit algorithms. This package is based on the algorithms by Liu et al. (2023) <arXiv:2311.14359>.

Version: 0.1.0
Imports: MASS, parallel, fastDummies, matrixStats, ggplot2, stats
Published: 2023-11-29
Author: Xueqing Liu [aut], Nina Deliu [aut], Tanujit Chakraborty ORCID iD [aut, cre, cph], Lauren Bell [aut], Bibhas Chakraborty [aut]
Maintainer: Tanujit Chakraborty <tanujitisi at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: countts results

Documentation:

Reference manual: countts.pdf

Downloads:

Package source: countts_0.1.0.tar.gz
Windows binaries: r-devel: countts_0.1.0.zip, r-release: countts_0.1.0.zip, r-oldrel: countts_0.1.0.zip
macOS binaries: r-release (arm64): countts_0.1.0.tgz, r-oldrel (arm64): countts_0.1.0.tgz, r-release (x86_64): countts_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=countts to link to this page.