The paper introduces the concept of Repeated Measures Correlation (rmcorr), a statistical technique designed to determine the common within-individual association for paired measures assessed on multiple occasions for multiple individuals. Unlike simple regression or correlation, which assume independence of observations, rmcorr does not violate this assumption and tends to have greater statistical power. The authors provide background information on rmcorr, including its assumptions, equations, visualization, and power calculations. They introduce the R package (rmcorr) and demonstrate its use with two example datasets, illustrating how rmcorr can be applied to intra-individual and inter-individual research questions. The paper highlights the advantages of rmcorr over simple regression/correlation, particularly in handling non-independent data and providing more accurate estimates of intra-individual associations. The authors also discuss the trade-offs between rmcorr and multilevel modeling, noting that while multilevel modeling can handle more complex designs, rmcorr is simpler and more suitable for assessing intra-individual associations. The paper concludes by emphasizing the utility of rmcorr for researchers dealing with paired repeated measures data and outlines future directions for the development of the rmcorr package.The paper introduces the concept of Repeated Measures Correlation (rmcorr), a statistical technique designed to determine the common within-individual association for paired measures assessed on multiple occasions for multiple individuals. Unlike simple regression or correlation, which assume independence of observations, rmcorr does not violate this assumption and tends to have greater statistical power. The authors provide background information on rmcorr, including its assumptions, equations, visualization, and power calculations. They introduce the R package (rmcorr) and demonstrate its use with two example datasets, illustrating how rmcorr can be applied to intra-individual and inter-individual research questions. The paper highlights the advantages of rmcorr over simple regression/correlation, particularly in handling non-independent data and providing more accurate estimates of intra-individual associations. The authors also discuss the trade-offs between rmcorr and multilevel modeling, noting that while multilevel modeling can handle more complex designs, rmcorr is simpler and more suitable for assessing intra-individual associations. The paper concludes by emphasizing the utility of rmcorr for researchers dealing with paired repeated measures data and outlines future directions for the development of the rmcorr package.