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 |
| 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 |
| Reference manual: | PricingBandits.html , PricingBandits.pdf |
| Vignettes: |
Getting Started with PricingBandits (source, R code) |
| Package source: | PricingBandits_2.0.0.tar.gz |
| Windows binaries: | r-devel: PricingBandits_2.0.0.zip, r-release: not available, r-oldrel: PricingBandits_2.0.0.zip |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): PricingBandits_2.0.0.tgz, r-release (x86_64): PricingBandits_2.0.0.tgz, r-oldrel (x86_64): PricingBandits_2.0.0.tgz |
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