v1.7.0

Added

• DBO backend (Dynamic Bayesian Optimization) for single-objective studies whose cost drifts during the session (participant adaptation, learning, fatigue). The GP covariance is multiplied by a temporal decay alpha^|t-t'| with the decay rate fitted by marginal likelihood, so the optimizer infers how fast the participant is changing; a DBO Stationary Baseline toggle pins alpha = 1 for the matched plain-BO control condition, and optional validation iterations apply the model's best estimate on a schedule so optimizers can be compared without their exploration policies confounding the result. New Backend -> DBO inspector option with a dedicated settings section, dbo.py/dbo_runtime.py backend speaking the standard socket protocol and writing the standard CSVs, plus DboDiagnosticsPerEvaluation.csv with the fitted alpha per iteration. Implementation is the BSD-3-Clause dbo-torch package (validated against the reference MATLAB DBO to ~1e-14), vendored under BOData/BayesianOptimization/dbo_torch/. Method: Kim & Sergi, Comput. Methods Biomech. Biomed. Eng. 2025 (doi 10.1080/10255842.2025.2595150) and IEEE RA-L 2026 (doi 10.1109/LRA.2026.3665072). See docs/dbo-backend.md; verify with python tests/dbo_protocol_check.py (manual, not part of unittest discovery).