Combinatorial Regression

Open-source · github.com/YuZh98/combinatorial-regression

Reproducibility code for the paper Statistical Modeling for Combinatorial Response Data. Includes the MH-within-Gibbs sampler, kernel comparison experiments, and waterfowl matching data analysis.

Quick Start

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# Sanity check
make smoke
make probit_smoke

# Main simulation study (MH-within-Gibbs)
make full

# Kernel comparison (supplementary)
make kernel_compare

# Data analysis (waterfowl matching)
make duck_full      # full model
make duck_reduced   # reduced model

Simulation study

The default make full runs both exponential and half-Gaussian kernels. To run only the main-paper kernel:

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JASA_METHODS=exponential make full

Outputs are saved under results/runs/mh_within_gibbs/.

Custom settings

All scripts support environment-variable overrides:

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JASA_N_ITER=20000 make full
JASA_METHODS=exponential,half_gaussian make full

Documentation

See REPRODUCIBILITY.md for detailed mapping between paper sections and scripts, and INSTALL.md for R and Python dependency setup.