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countts  

Thomson Sampling for Zero-Inflated Count Outcomes
View on CRAN: Click here


Download and install countts package within the R console
Install from CRAN:
install.packages("countts")

Install from Github:
library("remotes")
install_github("cran/countts")

Install by package version:
library("remotes")
install_version("countts", "0.1.0")



Attach the package and use:
library("countts")
Maintained by
Tanujit Chakraborty
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-11-29
Latest Update: 2023-11-29
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) .
How to cite:
Tanujit Chakraborty (2023). countts: Thomson Sampling for Zero-Inflated Count Outcomes. R package version 0.1.0, https://cran.r-project.org/web/packages/countts. Accessed 28 Jun. 2026.
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Complete documentation for countts
Functions, R codes and Examples using the countts R package
Some associated functions: apply_ZINB . apply_ZIP . apply_laplacePoisson . apply_linearTS . apply_normalNB . output_summary . update_algorithm . 
Some associated R codes: count_ts.R . globals.R .  Full countts package functions and examples
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