Learn R Programming

countts (version 0.1.0)

Thomson Sampling for Zero-Inflated Count Outcomes

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) .

Copy Link

Version

Install

install.packages('countts')

Monthly Downloads

147

Version

0.1.0

License

GPL (>= 2)

Maintainer

Tanujit Chakraborty

Last Published

November 29th, 2023

Functions in countts (0.1.0)

apply_normalNB

Apply the algorithms to make decisions for Thompson sampling Negative Binomial (TS-NB) algorithms
output_summary

Summarize the simulation results and generate the regret plot
apply_linearTS

Apply the algorithms to make decisions for Linear Thompson sampling (TS) algorithms
apply_ZIP

Apply the algorithms to make decisions for Thompson sampling Zero-inflated Poisson (TS-ZIP) algorithm
apply_laplacePoisson

Apply the algorithms to make decisions for Thompson sampling Poisson (TS-Poisson) algorithms
update_algorithm

Updating parameters in algorithm
apply_ZINB

Apply the algorithms to make decisions for Thompson sampling Zero-inflated Negative Binomial (TS-ZINB) algorithm