  The extreme-mass-ratio inspirals (EMRIs) of stellar-mass compact objects into
massive black holes are an important class of source for the future space-based
gravitational-wave detector LISA. Detecting signals from EMRIs will require
waveform models that are both accurate and computationally efficient. In this
paper, we present the latest implementation of an augmented analytic kludge
(AAK) model, publicly available at github.com/alvincjk/EMRI_Kludge_Suite as
part of an EMRI waveform software suite. This version of the AAK model has
improved accuracy compared to its predecessors, with two-month waveform
overlaps against a more accurate fiducial model exceeding 0.97 for a generic
range of sources; it also generates waveforms 5-15 times faster than the
fiducial model. The AAK model is well suited for scoping out data analysis
issues in the upcoming round of mock LISA data challenges. A simple analytic
argument shows that it might even be viable for detecting EMRIs with LISA
through a semi-coherent template bank method, while the use of the original
analytic kludge in the same approach will result in around 90% fewer
detections.
