PricingBandits 2.0.0

First public release of the streamlined package, extracted from the replication code for Weaver, Kumar, and Jain, “Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve” (Marketing Science).

• PricingBandit(): single entry point running one pricing experiment with any of six policies (“UCB”, “TS”, “GP-UCB”, “GP-TS”, “GP-UCB-M”, “GP-TS-M”), a user-supplied willingness-to-pay vector, and an arbitrary price grid.
• Heterogeneous-noise extension (hetero = TRUE) available for all Gaussian process variants, and periodic history resets (reset) for time-varying demand.
• Lightweight run diagnostics (GetDiagnostics()) counting how often numerical fallback paths fired; the counters draw no random numbers, so results are unaffected.
• num_knots defaults to 11 for the monotonic variants regardless of the number of arms (larger values can destabilize the truncated sampler).
• The truncated-sampling timeout (default 5 seconds per attempt) is a user-settable argument of PricingBandit().
• Results validated seed-for-seed bit-identical against the paper’s replication code for all policies.