The article introduces the R package **nparLD**, which provides robust rank-based methods for analyzing longitudinal data in factorial experiments. The package is designed to handle various factorial designs, including higher-way layouts, and offers user-friendly access to nonparametric procedures. The authors discuss the importance of robust methods in the presence of unknown distributional assumptions and outliers, highlighting the limitations of parametric and semiparametric procedures. They illustrate the implementation of the package through case studies from dentistry, biology, and medicine, demonstrating how to test hypotheses about treatment effects, time effects, and interactions using both Wald-type statistics (WTS) and ANOVA-type statistics (ATS). The article also includes a detailed explanation of the nonparametric marginal model and the interpretation of results, along with examples of how to use the package's functions to analyze specific designs. The authors conclude by outlining future plans for updating the package with new statistical procedures and improving its code structure.The article introduces the R package **nparLD**, which provides robust rank-based methods for analyzing longitudinal data in factorial experiments. The package is designed to handle various factorial designs, including higher-way layouts, and offers user-friendly access to nonparametric procedures. The authors discuss the importance of robust methods in the presence of unknown distributional assumptions and outliers, highlighting the limitations of parametric and semiparametric procedures. They illustrate the implementation of the package through case studies from dentistry, biology, and medicine, demonstrating how to test hypotheses about treatment effects, time effects, and interactions using both Wald-type statistics (WTS) and ANOVA-type statistics (ATS). The article also includes a detailed explanation of the nonparametric marginal model and the interpretation of results, along with examples of how to use the package's functions to analyze specific designs. The authors conclude by outlining future plans for updating the package with new statistical procedures and improving its code structure.