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) doi:10.48550/arXiv.2311.14359.
- Version0.1.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release11/29/2023
Team
Tanujit Chakraborty
Bibhas Chakraborty
Xueqing Liu
Show author detailsRolesAuthorNina Deliu
Show author detailsRolesAuthorLauren Bell
Show author detailsRolesAuthor
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- Imports4 packages