doi:10.48550/arXiv.2311.14359>.">

countts: Thomson Sampling for Zero-Inflated Count Outcomes (original) (raw)

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) <doi:10.48550/arXiv.2311.14359>.

Version: 0.1.0
Imports: MASS, parallel, fastDummies, matrixStats, ggplot2, stats
Published: 2023-11-29
DOI: 10.32614/CRAN.package.countts
Author: Xueqing Liu [aut], Nina Deliu [aut], Tanujit ChakrabortyORCID iD [aut, cre, cph], Lauren Bell [aut], Bibhas Chakraborty [aut]
Maintainer: Tanujit Chakraborty
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: countts results

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