Collection of exoplanet occurrence rate data from NASA's SAG 13
effort and Python scripts fitting occurrence rate models including stellar effective temperature to this data. Details
of these models are contained in Garrett et al. (2018). This work makes use of
emcee, a Python implementation of the affine-invariant ensemble sampler for Markov
chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010), written by
Dan Foreman-Mackey.
The data tables in the Data folder come from the SAG 13 Google Drive
folders Burke, Mulders, and Natalie9p1.
The data in these folders (stored as .npy files) are MCMC model parameter burn-in and sample data. The folder MCMC Data All contains
parameter burn-in and samples for occurrence rate models fit to all of the data in the Data folder. The folder MCMC Data FGK contains parameter burn-in and samples for occurrence rate models fit to F, G, and K type star data contained
in the Data folder. The folder MCMC Data M contains parameter burn-in and samples for occurrence rates fit only to M
type star data contained in the Data folder.
The Scripts folder contains scripts which perform model fitting using MCMC sampling for "simple" and "break radius"
models. Additional scripts are provided to display results of these fits.