fdasrvf: Elastic Functional Data Analysis for Phase and Amplitude Separation

James D. Tucker's fdasrvf package implements the square-root velocity framework for elastic functional data analysis. The method, based on research by Srivastava et al. (2011) and Tucker et al. (2014), performs alignment, PCA, and modeling of multidimensional and unidimensional functions. It is sourced from the paperswithcode platform.

Use Cases

• Perform elastic alignment of functional curves based on the square-root velocity framework.
• Conduct functional principal component analysis (PCA) on aligned amplitude components.
• Model functional data where phase and amplitude variations need to be separated.
• Analyze time-series or continuous data where traditional rigid alignment is insufficient.

Strengths

• Implements a published methodological framework with specific citations (Srivastava et al., 2011; Tucker et al., 2014).
• Description specifies core capabilities: alignment, PCA, and modeling of functional data.

Limitations

• Row count, column definitions, and sample data are unknown, which limits suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Software package for functional data analysis.