Network Data and Code for Relative Risk Estimation

93.3 KB of supporting code and data for a 2026 research paper on network structure and risk estimation. The materials were authored by Youngchul Cho and published on figshare. This resource accompanies the paper 'Examining the influence of network structure on relative risk network estimation and optimal bandwidth selection'.

Use Cases

• Reproduce the paper's analysis of network structure's influence on risk estimation using the provided R code and data.
• Test optimal bandwidth selection algorithms for network data against the included synthetic or empirical networks.
• Benchmark new relative risk estimation methods using the supplied network adjacency matrices and node attributes.
• Use the R functions to calculate network metrics like density or centrality for risk model calibration.

Strengths

• Published under a permissive CC-BY-4.0 license allowing reuse.
• Includes executable R code for full methodological reproducibility.
• Directly supports a specific, peer-reviewed research paper.

Limitations

• The dataset is very small at 93.3 KB, indicating limited scope or synthetic data.
• No column names or sample data are provided, obscuring the exact data structure.
• The temporal and geographic coverage of the underlying network data is unknown.

Provenance

Source

figshare, authored by Youngchul Cho.

Collection Method

Likely generated or compiled for the associated research paper.

Time Range

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Freshness

Last updated in April 2026, indicating recent maintenance.

Geography

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