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) .


Reference manual

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install.packages("countts")

0.1.0 by Tanujit Chakraborty, 3 years ago


Browse source code at https://github.com/cran/countts


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


Documentation:   PDF Manual  


GPL (>= 2) license


Imports MASS, parallel, fastDummies, matrixStats, ggplot2, stats


See at CRAN