michaelschwob / composite-dyadic-models

Julia and R Code for Schwob, Hooten, Narasimhan, "Composite Dyadic Models for Spatio-Temporal Data," in review.

Home Page:https://arxiv.org/abs/2311.01341

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composite-dyadic-models

Julia and R Code for Schwob, Hooten, Narasimhan, "Composite Dyadic Models for Spatio-Temporal Data," in review.

aDNA

Contains the code used to analyze the Bronze Age Europe human genomes.

  • changePops.R : changes the "population identifier" for each genome to an individual identifier to compute genetic dissimilarites between all individuals
  • filter.R : takes the subset of data that we used for analysis in our paper
  • CRPS.jl : computes the continuous rank probability scores (CRPSs) for the competing models
  • mcmc_both/mcmc_ds/mcmc_dt/mcmc_none.jl : contains the MCMC algorithm for our Bayesian hierarchical model fully specified in Web Appendix B with the corresponding composite weighting scheme
  • meta.jl : the master script that runs all other scripts
  • plotting.jl : plots the potential surface and MCMC diagnostics
  • script.jl : structures the data for use in the MCMC algorithm

The aDNA data is made publicly available at the Allen Ancient DNA Resource.

ar1

Contains the code used to simulate the mechanistic population dynamic model in Web Appendix A.

  • ar1.r : simulates data from the model, analyzes the simulated data via MCMC, and (when using composite weights) adequately recovers the true parametric values

rupicapra

Contains the code used to analyze the R. rupicapra landscape genomic data in Web Appendix C.

  • CRPS.jl : computes the continuous rank probability scores (CRPSs) for the competing models
  • mcmc_ds/mcmc_none.jl : contains the MCMC algorithm for our Bayesian hierarchical model fully specified in Web Appendix B with the corresponding composite weighting scheme
  • plotting.jl : plots the potential surface and MCMC diagnostics
  • script.jl : structures the data for use in the MCMC algorithm

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Julia and R Code for Schwob, Hooten, Narasimhan, "Composite Dyadic Models for Spatio-Temporal Data," in review.

https://arxiv.org/abs/2311.01341


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