Thomson Sampling for Zero-Inflated Count Outcomes R package countts version 0.1.0 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>. Software Imports: MASS, parallel, fastDummies, matrixStats, ggplot2, stats Tanujit Chakraborty <tanujitisi@gmail.com> Comprehensive R Archive Network (CRAN) Xueqing Liu [aut], Nina Deliu [aut], Tanujit Chakraborty [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-3479-2187>), Lauren Bell [aut], Bibhas Chakraborty [aut] GPL (>= 2) 2023-11-29 application/tgz https://CRAN.R-project.org/package=countts doi:10.32614/CRAN.package.countts