Package: countts 0.1.0

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>.

Authors:Xueqing Liu [aut], Nina Deliu [aut], Tanujit Chakraborty [aut, cre, cph], Lauren Bell [aut], Bibhas Chakraborty [aut]