The paper introduces the R package nparLD, which provides robust nonparametric methods for analyzing longitudinal data in factorial experiments. Longitudinal data from factorial experiments are common in various fields, but traditional parametric methods often assume specific distributions, which may not hold in practice. Nonparametric methods, which do not rely on distributional assumptions, are therefore more suitable for such data. The nparLD package offers user-friendly access to these methods, enabling researchers to analyze longitudinal data with minimal assumptions. The paper discusses three factorial longitudinal designs: LD-F1, F1-LD-F1, and F2-LD-F1. For each design, nonparametric hypotheses are formulated in terms of distribution functions rather than expectations. The package implements rank-based methods for testing these hypotheses and estimating relative treatment effects. The results are presented using Wald-type (WTS) and ANOVA-type (ATS) statistics, which are used to assess treatment, time, and interaction effects. The paper presents three case studies: a dental study on growth curves, a rat growth study, and a respiratory disorder study. In each case, the nparLD package is used to analyze longitudinal data, and the results are compared with parametric methods. The nonparametric approach is shown to be robust to outliers and skewed data, and to provide accurate results even with small sample sizes. In the respiratory disorder study, the nonparametric results indicated significant treatment and time effects, while the parametric method suggested no significant time effect. This highlights the importance of using nonparametric methods when data do not meet the assumptions of parametric procedures. The paper concludes that the nparLD package is a valuable tool for analyzing longitudinal data in factorial experiments, offering a flexible and robust alternative to traditional parametric methods. Future work includes updating the package with new nonparametric procedures and implementing multiple contrast testing methods. The package is freely available from CRAN and is designed to be user-friendly for a wide audience of statisticians and researchers.The paper introduces the R package nparLD, which provides robust nonparametric methods for analyzing longitudinal data in factorial experiments. Longitudinal data from factorial experiments are common in various fields, but traditional parametric methods often assume specific distributions, which may not hold in practice. Nonparametric methods, which do not rely on distributional assumptions, are therefore more suitable for such data. The nparLD package offers user-friendly access to these methods, enabling researchers to analyze longitudinal data with minimal assumptions. The paper discusses three factorial longitudinal designs: LD-F1, F1-LD-F1, and F2-LD-F1. For each design, nonparametric hypotheses are formulated in terms of distribution functions rather than expectations. The package implements rank-based methods for testing these hypotheses and estimating relative treatment effects. The results are presented using Wald-type (WTS) and ANOVA-type (ATS) statistics, which are used to assess treatment, time, and interaction effects. The paper presents three case studies: a dental study on growth curves, a rat growth study, and a respiratory disorder study. In each case, the nparLD package is used to analyze longitudinal data, and the results are compared with parametric methods. The nonparametric approach is shown to be robust to outliers and skewed data, and to provide accurate results even with small sample sizes. In the respiratory disorder study, the nonparametric results indicated significant treatment and time effects, while the parametric method suggested no significant time effect. This highlights the importance of using nonparametric methods when data do not meet the assumptions of parametric procedures. The paper concludes that the nparLD package is a valuable tool for analyzing longitudinal data in factorial experiments, offering a flexible and robust alternative to traditional parametric methods. Future work includes updating the package with new nonparametric procedures and implementing multiple contrast testing methods. The package is freely available from CRAN and is designed to be user-friendly for a wide audience of statisticians and researchers.