  Fulfilling the rich promise of rapid advances in time-domain astronomy is
only possible through confronting our observations with physical models and
extracting the parameters that best describe what we see. Here, we introduce
{\sc Redback}; a Bayesian inference software package for electromagnetic
transients. {\sc Redback} provides an object-orientated {\sc python} interface
to over 12 different samplers and over 100 different models for kilonovae,
supernovae, gamma-ray burst afterglows, tidal disruption events, engine-driven
transients among other explosive transients. The models range in complexity
from simple analytical and semi-analytical models to surrogates built upon
numerical simulations accelerated via machine learning. {\sc Redback} also
provides a simple interface for downloading and processing data from various
catalogs such as \textit{Swift} and Fink. The software can also serve as an
engine to simulate transients for telescopes such as the Zwicky Transient
Facility and Vera Rubin with realistic cadences, limiting magnitudes, and
sky-coverage or a hypothetical user-constructed survey or a generic transient
for target-of-opportunity observations with different telescopes. As a
demonstration of its capabilities, we show how {\sc Redback} can be used to
jointly fit the spectrum and photometry of a kilonova, enabling a more
powerful, holistic probe into the properties of a transient. We also showcase
general examples of how {\sc Redback} can be used as a tool to simulate
transients for realistic surveys, fit models to real, simulated, or private
data, multi-messenger inference with gravitational waves, and serve as an
end-to-end software toolkit for parameter estimation and interpreting the
nature of electromagnetic transients.
