This study develops and evaluates a computational framework for quantifying the metrical organization of Shakespeare's complete sequence of 154 sonnets. Rather than treating poetic metre as a collection of individually interpreted deviations, the framework models the relationship between lexical stress and iambic pentameter as a reproducible binary sequential structure. The corpus comprises 2,155 poetic lines, subjected to pronunciation normalization, Early Modern English correction, lexical-stress extraction, alternative metrical alignment, and minimum-deviation optimization. Automated stress assignment achieves 95.1\% agreement with independently verified annotations, providing a validated basis for subsequent corpus-level analysis. The resulting metrical representations are examined through deviation-density measures, Shannon entropy, position-specific entropy, first-order Markov transitions, principal component analysis, hierarchical clustering, recurrence analysis, and Monte Carlo hypothesis testing. The empirical results reveal a metrical system characterized by strong regularity without mechanical uniformity. Deviations exhibit pronounced positional heterogeneity, while the transition from deviation to metrical agreement occurs with probability 0.8778, indicating strong local recovery following departures from the reference pattern. Multivariate analysis shows that metrical variability is distributed across multiple dimensions rather than governed by a single […]
