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 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=countts to link to this page.
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