pyOMA2: A Python module for conducting operational modal analysis

[1.4.3] - 2026-09-22 Added MultiSetup_PoSER.merge_results() accepts freq_tol and mac_min to tune how modes are paired across setups; MsPoserResult.setups_used lists the setups that identified each merged mode. gen.match_modes pairs the modes of different setups by frequency and reference-sensor MAC. Fixed PoSER merge paired modes by index, so modes identified in a different order were averaged with a different physical mode; they are now paired by frequency and reference-sensor MAC. merge_mode_shapes scaled the roving sensors by the inverse Modal Scale Factor; each setup is now scaled onto the reference sensors of the first setup. PoSER merge failed when a mode was missing in some setups; the mode is now merged from the setups that identified it, with NaN roving sensors for the others. PoSER merge raised AttributeError for algorithms without damping (FDD); the merged