Package: PricingBandits 2.1.0

PricingBandits: Multi-Armed Bandit Approaches to Pricing Experiments

Implements multi-armed bandit approaches for pricing experiments with an unknown demand curve, as developed in Weaver, Kumar, and Jain (2025) "Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve" <doi:10.1287/mksc.2022.0247>. Includes Upper Confidence Bound (UCB) and Thompson Sampling (TS) baselines, Gaussian process variants (GP-UCB, GP-TS), monotonic Gaussian process variants that constrain demand to be weakly decreasing in price, and heterogeneous-noise extensions. The willingness-to-pay distribution is fully user-specified via a vector of consumer valuations, so any demand environment can be simulated or replayed.

Authors:Ian N. Weaver [aut, cre], Vineet Kumar [aut], Lalit Jain [aut]