ARRP

ARRP performs asymptotic and robust regression analyses for constant and linear asymptotic regression models to provide objective parameter estimation for statistical mechanics applications in biological sequence alignment, including DNA and protein contexts and models related to directed polymers in random media.

Key Features:

• Objective Change-Point Detection: Implements change-point detection algorithms to identify the asymptotic regime where regression models are applicable, removing subjective assessment.
• Asymptotic and Robust Regression: Performs both asymptotic and robust regression for constant and linear asymptotic regression models to estimate parameters relevant to sequence alignment statistics.
• Efficient Computation: Reduces computation time for statistical analyses associated with sequence alignments by leveraging recent computational methods.
• Cross-Disciplinary Applicability: Applies to biological (DNA and protein annealing approximations) and physical systems such as directed polymers in random media.

Scientific Applications:

• Biological Sequence Alignment: Provides precise regression coefficients and objective asymptotic regime identification to refine statistical mechanics models used in aligning DNA and protein sequences.
• Statistical Mechanics Models: Supplies objective parameter estimates for asymptotic regimes in models of complex systems, including directed polymers in random media.

Methodology:

Performs asymptotic and robust regression analyses for constant and linear models and integrates change-point detection algorithms to identify the regime where regression models hold for objective parameter estimation.