Our package is well suited to the analysis of small to modest sized data sets, with no regard to the sampling which may be even or uneven. Thus our package suits astronomers who often have to deal with unevenly sampled observations well. One of the advantages of the package is the availability of tools for a statistical evaluation of the results.
Data sets containing many observations but covering only few cycles
and/or characteristic time intervals can be reduced in number by
averaging or decimation, usually with little loss of information.
However, the analysis of very extensive datasets, which cover many
cycles, contain, say, over 
observations and/or are sampled
evenly, is more demanding in terms of computing efficiency than in the
choice of the method. With the present package, MIDAS offers an
excellent general purpose environment and a variety of tools for the
analysis of astronomical data at the price of some computing
overheads. Very large data sets usually concern important problems and
therefore deserve extra attention in the analysis. For such cases any
extra overhead is undesirable, whereas extra efficiency can be gained
from specialized algorithms implemented as purpose-built stand-alone
codes. One class of such specialized algorithms not covered here is based
upon the fast Fourier transform technique (see e.g. Bloomfield, 1976,
Press and Rybicki, 1991).