Supplement 1. R code containing the algorithms described in this paper, as applied to the constant, known food scenario.

File List deb_mcmc.R (MD5: 24027cecfc5fc9158b906a8a1d96613f) - R code implementing the MCMC itself<br> deb_model.R (MD5: 67486b9d95fe61db1ddb4add2f89a614) - R code specifying the differential equation model<br> deb_plotting.R (MD5: 4d96e43bc3758b76a81a28c2165442a3) - functions for plotting results <br> deb_post_prior.R (MD5: 0244a22b8855fa8401538680126a2343) - R code with implementation of the priors and posterior distribution<br> deb_solver.R (MD5: ced3a9e359d168038c5c57d5aa93f3d3) - R code with functions to solve the differential equations <br> run_mcmc_knowncf.R (MD5: 3b4d3e1f6931b4bff0f55c216756faa3) - R code that calls everything above and performs the MCMC<br> deb_infer.zip (MD5: 5fb070df6d56a385f21d883f38b1972a) - All files at onceDescription The R code in the file run_mcmc_knowncf.R calls the other files to first simulate data with from the model described in the paper with known parameters (implemented in deb_model.R), and then perform Bayesian inference of the parameters using a Metropolis-within-Gibbs type Markov chain Monte Carlo method (described in the Appendices and implemented in deb_mcmc.R) for the likelihood and priors implemented in deb_post_prior.R. The code requires that the PBSddesolve package cran) be installed.