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>.
| Version: |
0.1.0 |
| Imports: |
MASS, parallel, fastDummies, matrixStats, ggplot2, stats |
| Published: |
2023-11-29 |
| Author: |
Xueqing Liu [aut],
Nina Deliu [aut],
Tanujit Chakraborty
 [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 |
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