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>.

Solves optimal pairing and matching problems using linear assignment
    algorithms. Provides implementations of the Hungarian method (Kuhn 1955)
    <doi:10.1002/nav.3800020109>, Jonker-Volgenant shortest path algorithm
    (Jonker and Volgenant 1987) <doi:10.1007/BF02278710>, Auction algorithm
    (Bertsekas 1988) <doi:10.1007/BF02186476>, cost-scaling
    (Goldberg and Kennedy 1995) <doi:10.1007/BF01585996>, scaling algorithms
    (Gabow and Tarjan 1989) <doi:10.1137/0218069>, push-relabel (Goldberg and
    Tarjan 1988) <doi:10.1145/48014.61051>, and Sinkhorn entropy-regularized
    transport (Cuturi 2013) <doi:10.48550/arxiv.1306.0895>. Designed for
    matching plots, sites, samples, or any pairwise optimization problem.
    Supports rectangular matrices, forbidden assignments, data frame inputs,
    batch solving, k-best solutions, and pixel-level image morphing for
    visualization. Includes automatic preprocessing with variable health
    checks, multiple scaling methods (standardized, range, robust), greedy
    matching algorithms, and comprehensive balance diagnostics for assessing
    match quality using standardized differences and distribution comparisons.

Easily install and load all packages and functions used in
    'CourseKata' courses. Aid teaching with helper functions and augment
    generic functions to provide cohesion between the network of packages.
    Learn more about 'CourseKata' at <https://www.coursekata.org>.