rMEA

rMEA analyzes Motion Energy Analysis (MEA) time-series to quantify and assess nonverbal synchrony and time-lagged movement relationships between individuals.

Key Features:

• Data importation and visualization: Imports dyadic time-series generated by MEA software and provides visualization for diagnostics, analysis, and interpretation of nonverbal behavior data.
• Windowed cross-correlation with lag analysis: Implements a fast moving-window cross-correlation algorithm with lag analysis to assess dynamic, time-lagged synchrony between subjects.
• Surrogate data generation: Generates surrogate datasets to estimate pseudosynchrony and to compute effect sizes for observed synchronies.

Scientific Applications:

• Psychotherapy research: Analyze nonverbal synchrony in patient–therapist dyads to investigate therapeutic alliance and treatment outcomes.
• Interpersonal interaction research: Study relationships between movement synchrony and constructs such as relationship quality, empathy, and other relational outcomes.

Methodology:

Import MEA-generated time-series into R; apply moving-window cross-correlation with lag analysis for dynamic assessment; generate surrogate data to estimate pseudosynchrony and effect sizes; produce visual diagnostics for interpretation.