Package: countts
Title: Thomson Sampling for Zero-Inflated Count Outcomes
Version: 0.1.0
Authors@R: c(
    person("Xueqing", "Liu", email = "xueqing_liu@u.duke.nus.edu", role = "aut"),
    person("Nina", "Deliu", email = "nina.deliu@uniromal.it", role = "aut"),
    person("Tanujit", "Chakraborty", email = "tanujitisi@gmail.com", role = c("aut", "cre","cph"),
           comment = c(ORCID = "0000-0002-3479-2187")),
    person("Lauren", "Bell", email = "lauren.bell@mrc-bsu.cam.ac.uk", role = "aut"),
    person("Bibhas", "Chakraborty", email = "bibhas.chakraborty@duke-nus.edu.sg", role = "aut"))
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>.
Maintainer: Tanujit Chakraborty <tanujitisi@gmail.com>
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: MASS, parallel, fastDummies, matrixStats, ggplot2, stats
NeedsCompilation: no
Packaged: 2023-11-29 04:15:35 UTC; mad-s
Author: Xueqing Liu [aut],
  Nina Deliu [aut],
  Tanujit Chakraborty [aut, cre, cph]
    (<https://orcid.org/0000-0002-3479-2187>),
  Lauren Bell [aut],
  Bibhas Chakraborty [aut]
Repository: CRAN
Date/Publication: 2023-11-29 14:00:10 UTC
Built: R 4.2.3; ; 2023-11-29 15:29:07 UTC; unix
