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In Kato and Uemura (2012), we introduced the Least Absolute Shrinkage andSelection Operator (Lasso) method, a kind of sparse modeling, to studyfrequency structures of variable stars. A very high frequency resolution wasachieved compared to traditional Fourier-type frequency analysis. This methodhas been extended to two-dimensional frequency analysis to obtain dynamicspectra. This two-dimensional Lasso frequency analysis yielded a wide range ofresults including separation of the orbital, superhump and negative superhumpsignals in Kepler data of SU UMa stars. In this paper, I briefly reviewed theprogress and applications of this method. I present a full R code with examplesof its usage. This code has been confirmed to detect the appearance of theorbital signal and the variation of the spin period after the eruption of thenova V1674 Her. This code also can be used in multidisciplinary purposes and Iprovide applications to analysis of avian vocalizations. I found finestructures in the call of the Eurasian wren ($\textit{Troglodytestroglodytes}$), which is likely to be used species identification. This codewould be a new tool in studying avian vocalizations with high temporal andfrequency resolutions. Interpretations of the power spectra of avianvocalizations will also be helpful in interpreting the power spectra ofvariable stars.

A code for two-dimensional frequency analysis using the Least Absolute Shrinkage and Selection Operator (Lasso) for multidisciplinary use