countts

Description

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>.

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Maintainer: ‘Tanujit Chakraborty <tanujitisi@gmail.com>’

The Description field contains
  is based on the algorithms by Liu et al. (2023) <arXiv:2311.14359>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Tanujit Chakraborty <tanujitisi@gmail.com>’

The Description field contains
  is based on the algorithms by Liu et al. (2023) <arXiv:2311.14359>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘Tanujit Chakraborty <tanujitisi@gmail.com>’

The Description field contains
  is based on the algorithms by Liu et al. (2023) <arXiv:2311.14359>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Tanujit Chakraborty <tanujitisi@gmail.com>’

The Description field contains
  is based on the algorithms by Liu et al. (2023) <arXiv:2311.14359>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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