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,
"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.
| Version: |
2.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, Matrix, hash, nloptr, MASS, dplyr, TruncatedNormal, R.utils |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, ggplot2 |
| Published: |
2026-09-09 |
| DOI: |
10.32614/CRAN.package.PricingBandits (may not be active yet) |
| Author: |
Ian N. Weaver [aut, cre],
Vineet Kumar [aut],
Lalit Jain [aut] |
| Maintainer: |
Ian N. Weaver <weaver.n.ian at gmail.com> |
| BugReports: |
https://github.com/ian-weaver/PricingBandits/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/ian-weaver/PricingBandits |
| NeedsCompilation: |
no |
| Citation: |
PricingBandits citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
PricingBandits results |
Documentation:
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