# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "PricingBandits" in publications use:' type: software license: MIT title: 'PricingBandits: Multi-Armed Bandit Approaches to Pricing Experiments' version: 2.1.0 doi: 10.1287/mksc.2022.0247 identifiers: - type: doi value: 10.32614/CRAN.package.PricingBandits abstract: 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" . 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: - family-names: Weaver given-names: Ian N. email: weaver.n.ian@gmail.com - family-names: Kumar given-names: Vineet - family-names: Jain given-names: Lalit preferred-citation: type: article title: Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve authors: - family-names: Weaver given-names: Ian N. email: weaver.n.ian@gmail.com - family-names: Kumar given-names: Vineet - family-names: Jain given-names: Lalit journal: Marketing Science year: '2025' doi: 10.1287/mksc.2022.0247 repository: https://ian-weaver.r-universe.dev repository-code: https://github.com/ian-weaver/PricingBandits commit: 72f87451d2a9947fd1f47d55057aad4975fbc849 url: https://github.com/ian-weaver/PricingBandits date-released: '2026-09-10' contact: - family-names: Weaver given-names: Ian N. email: weaver.n.ian@gmail.com