[This article was first published on r-bloggers on Francisco Bischoff, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.img { display: block; margin-left: auto; margin-right: auto;}A new chapter for the Matrix Profile in RFor a while, tsmp looked quiet. The project was not standing still, though: the Matrix Profile tools for R have been reorganized around a much clearer split between the computational engine and the everyday data-mining interface.In tsmp 0.5.1, the package that many of us have used for years becomes a pure-R analytical layer. The new matrixprofiler package takes over the computationally intensive work in an optimized C++17 engine. The result is not a replacement for tsmp; it is a stronger foundation underneath it.Two packages, two jobsThe older tsmp release brought algorithms, analysis helpers, and much of the computation together in one package. That made it a convenient starting point, but as series and workloads grew, computational work implemented around R-level operations could become the bottleneck.The new architecture separates those responsibilities:matrixprofiler provides the low-level Matrix Profile algorithms and optimized numerical primitives, with native multithreading through RcppThread.tsmp keeps the higher-level workflow close to the analyst: motifs, chains, segmentation, snippets, plotting, and the familiar tools for day-to-day time-series mining.The heavy computation now runs in the native engine, while the package you use to explore and interpret results stays approachable. tsmp itself no longer needs compilation when installed from source; it delegates its computational backend to matrixprofiler.The everyday workflow remains familiar:library(tsmp)profile