  The statistical power of Lyman-${\alpha}$ forest Baryon Acoustic Oscillation
(BAO) measurements is set to increase significantly in the coming years as new
instruments such as the Dark Energy Spectroscopic Instrument deliver
progressively more constraining data. Generating mock datasets for such
measurements will be important for validating analysis pipelines and evaluating
the effects of systematics. With such studies in mind, we present LyaCoLoRe: a
package for producing synthetic Lyman-${\alpha}$ forest survey datasets for BAO
analyses. LyaCoLoRe transforms initial Gaussian random field skewers into
skewers of transmitted flux fraction via a number of fast approximations. In
this work we explain the methods of producing mock datasets used in LyaCoLoRe,
and then measure correlation functions on a suite of realisations of such data.
We demonstrate that we are able to recover the correct BAO signal, as well as
large-scale bias parameters similar to literature values. Finally, we briefly
describe methods to add further astrophysical effects to our skewers - high
column density systems and metal absorbers - which act as potential
complications for BAO analyses.
