  In Kato and Uemura (2012), we introduced the Least Absolute Shrinkage and
Selection Operator (Lasso) method, a kind of sparse modeling, to study
frequency structures of variable stars. A very high frequency resolution was
achieved compared to traditional Fourier-type frequency analysis. This method
has been extended to two-dimensional frequency analysis to obtain dynamic
spectra. This two-dimensional Lasso frequency analysis yielded a wide range of
results including separation of the orbital, superhump and negative superhump
signals in Kepler data of SU UMa stars. In this paper, I briefly reviewed the
progress and applications of this method. I present a full R code with examples
of its usage. This code has been confirmed to detect the appearance of the
orbital signal and the variation of the spin period after the eruption of the
nova V1674 Her. This code also can be used in multidisciplinary purposes and I
provide applications to analysis of avian vocalizations. I found fine
structures in the call of the Eurasian wren ($\textit{Troglodytes
troglodytes}$), which is likely to be used species identification. This code
would be a new tool in studying avian vocalizations with high temporal and
frequency resolutions. Interpretations of the power spectra of avian
vocalizations will also be helpful in interpreting the power spectra of
variable stars.
