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PricingBandits  

Multi-Armed Bandit Approaches to Pricing Experiments
View on CRAN: Click here


Download and install PricingBandits package within the R console
Install from CRAN:
install.packages("PricingBandits")

Install from Github:
library("remotes")
install_github("cran/PricingBandits")

Install by package version:
library("remotes")
install_version("PricingBandits", "2.0.0")



Attach the package and use:
library("PricingBandits")
Maintained by
Ian N. Weaver
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-09
Latest Update: 2026-09-09
Description:
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.
How to cite:
Ian N. Weaver (2026). PricingBandits: Multi-Armed Bandit Approaches to Pricing Experiments. R package version 2.0.0, https://cran.r-project.org/web/packages/PricingBandits. Accessed 24 Oct. 2026.
Previous versions and publish date:
(2026-10-20 16:40), 2.0.0 (2026-09-09 16:30)
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Complete documentation for PricingBandits
Functions, R codes and Examples using the PricingBandits R package
Full PricingBandits package functions and examples
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