Package: countts 0.1.0

Tanujit Chakraborty

countts: 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) <arxiv:2311.14359>.

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

countts_0.1.0.tar.gz
countts_0.1.0.tar.gz(r-4.5-noble)countts_0.1.0.tar.gz(r-4.4-noble)
countts_0.1.0.tgz(r-4.4-emscripten)countts_0.1.0.tgz(r-4.3-emscripten)
countts.pdf |countts.html✨
countts/json (API)

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7 exports 0.00 score 33 dependencies 165 downloads

Last updated 10 months agofrom:4edec682fc. Checks:OK: 2. Indexed: yes.

Exports:apply_laplacePoissonapply_linearTSapply_normalNBapply_ZINBapply_ZIPoutput_summaryupdate_algorithm

Dependencies:clicolorspacedata.tablefansifarverfastDummiesggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalesstringistringrtibbleutf8vctrsviridisLitewithr