Statistical Shape Analysis Routines for Landmark Data

Routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, and principal components analysis. The dataset is based on methods from the 2016 textbook 'Statistical Shape Analysis, with Applications in R' by Ian Dryden and K.V. Mardia. It is hosted on the Papers with Code platform.

Use Cases

• Perform Procrustes superimposition to align and compare shapes based on landmark coordinates.
• Conduct principal components analysis to explore major modes of shape variation within a sample.
• Run permutation and bootstrap tests for statistical inference on shape differences between groups.
• Visualize shape deformations using thin-plate spline transformation grids.
• Compare covariance matrices to assess differences in shape variation patterns.

Strengths

• Based on established textbook methodology from a 2016 second edition.
• Includes a suite of complementary analysis routines (e.g., PCA, permutation tests, visualization).

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Ian Dryden, author of the referenced textbook.

Collection Method

Likely a collection of software routines implementing statistical shape analysis methods.