Repeated measures correlation (rmcorr) is a statistical method for analyzing the common within-individual association between paired measures collected over multiple occasions. Unlike traditional regression/correlation, which assumes independence between observations, rmcorr accounts for non-independence by using analysis of covariance (ANCOVA) to adjust for inter-individual variability. This approach provides a more accurate estimate of the common regression slope, representing the strength of the linear relationship between two variables. rmcorr is particularly useful for intra-individual analysis, as it does not require data averaging, thus maintaining statistical power and avoiding bias from aggregation. The method is compared to multilevel modeling, which can also analyze variance at multiple levels but requires more complex data and is harder to interpret. The rmcorr R package is introduced, offering tools for inferential statistics and visualization. Two example datasets are used to demonstrate rmcorr's application in assessing intra-individual and inter-individual associations. The first example examines the relationship between age and brain structure volume, showing a stronger intra-individual association using rmcorr than simple regression. The second example analyzes visual search data, revealing a negative relationship between speed and accuracy using rmcorr, while aggregated data may obscure this relationship. rmcorr is well-suited for research questions involving common linear associations in paired repeated measures data, with results fully reproducible. The method is recommended for studies where non-independent observations are common, as it provides greater statistical power and avoids the pitfalls of averaging data.Repeated measures correlation (rmcorr) is a statistical method for analyzing the common within-individual association between paired measures collected over multiple occasions. Unlike traditional regression/correlation, which assumes independence between observations, rmcorr accounts for non-independence by using analysis of covariance (ANCOVA) to adjust for inter-individual variability. This approach provides a more accurate estimate of the common regression slope, representing the strength of the linear relationship between two variables. rmcorr is particularly useful for intra-individual analysis, as it does not require data averaging, thus maintaining statistical power and avoiding bias from aggregation. The method is compared to multilevel modeling, which can also analyze variance at multiple levels but requires more complex data and is harder to interpret. The rmcorr R package is introduced, offering tools for inferential statistics and visualization. Two example datasets are used to demonstrate rmcorr's application in assessing intra-individual and inter-individual associations. The first example examines the relationship between age and brain structure volume, showing a stronger intra-individual association using rmcorr than simple regression. The second example analyzes visual search data, revealing a negative relationship between speed and accuracy using rmcorr, while aggregated data may obscure this relationship. rmcorr is well-suited for research questions involving common linear associations in paired repeated measures data, with results fully reproducible. The method is recommended for studies where non-independent observations are common, as it provides greater statistical power and avoids the pitfalls of averaging data.