  This and companion papers by Harrington et al. 2021, submitted and Cubillos
et al. 2021, submitted describe an open-source retrieval framework, Bayesian
Atmospheric Radiative Transfer (BART), available to the community under the
reproducible-research license via https://github.com/exosports/BART . BART is a
radiative-transfer code (transit, https://github.com/exosports/transit , Rojo
2009, 2009ASPC..420..321R), initialized by the Thermochemical Equilibrium
Abundances (TEA, https://github.com/dzesmin/TEA , Blecic et al. 2016,
arXiv:1505.06392) code, and driven through the parameter phase space by a
differential-evolution Markov-chain Monte Carlo (MC3,
https://github.com/pcubillos/mc3 , Cubillos et al. 2017, arXiv:1610.01336)
sampler. In this paper we give a brief description of the framework, and its
modules that can be used separately for other scientific purposes; outline the
retrieval analysis flow; present the initialization routines, describing in
detail the atmospheric profile generator and the temperature and species
parameterizations; and specify the post-processing routines and outputs,
concentrating on the spectrum band integrator, the best-fit model selection,
and the contribution functions. We also present an atmospheric analysis of
WASP-43b secondary eclipse data obtained from space- and ground-based
observations. We compare our results with the results from the literature, and
investigate how the inclusion of additional opacity sources influence the
best-fit model.
