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We describe the $\texttt{Period detection and Identification Pipeline Suite}$($\texttt{PIPS}$) -- a new, fast, and statistically robust platform for perioddetection and analysis of astrophysical time-series data. $\texttt{PIPS}$ is anopen-source Python package that provides various pre-implemented methods and acustomisable framework for automated, robust period measurements withprincipled uncertainties and statistical significance calculations. In additionto detailing the general algorithm that underlies $\texttt{PIPS}$, this paperdiscusses one of $\texttt{PIPS'}$ central and novel features, theFourier-likelihood periodogram, and compares its performance to existingmethods. The resulting improved performance implies that one can constructdeeper, larger, and more reliable sets of derived properties from variousobservations, including all-sky surveys. We present a comprehensive validationof $\texttt{PIPS}$ against artificially generated data, which demonstrates thereliable performance of our algorithm for a class of periodic variable stars(RR Lyrae stars).

PIPS, an advanced platform for period detection in time series -- I. Fourier-likelihood periodogram and application to RR Lyrae Stars