Multi-Armed Bandit Approaches to Pricing Experiments R package PricingBandits version 2.0.0 Implements multi-armed bandit approaches for pricing experiments with an unknown demand curve, as developed in Weaver, Kumar, and Jain, "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. Software Depends: R (>= 3.5.0) Imports: stats, Matrix, hash, nloptr, MASS, dplyr, TruncatedNormal, R.utils Suggests: testthat (>= 3.0.0), knitr, rmarkdown, ggplot2 Ian N. Weaver <weaver.n.ian@gmail.com> Comprehensive R Archive Network (CRAN) Ian N. Weaver [aut, cre], Vineet Kumar [aut], Lalit Jain [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=PricingBandits/LICENSE) 2026-09-09 application/tgz https://CRAN.R-project.org/package=PricingBandits doi:10.32614/CRAN.package.PricingBandits