Time course measurements are used for many applications in biomedical research, ranging from growth curves to drug efficacy testing and high-throughput screening. Statistical methods used to analyze the resulting longitudinal data, such as t-tests or repeated measures ANOVA have limitations when groups are unbalanced, or individual measurements are missing. To address these issues we developed biogrowleR (h t t p s : / / u p b r i. g i t l a b. i o / b i o g r o w l e R /), a workflow to visualize and analyze data based on Frequentist and Bayesian inference combined with hierarchical modeling. By focusing on effect sizes we enhance data interpretation. The workflow further includes a randomization algorithm important to reduce numbers of experimental animals (RRR) and costs. The workflow and R package were designed to be used by researchers with limited experience in R and biostatistics. Our open-source R package biogrowleR contains tutorials, pipelines, and helper functions for the analysis of longitudinal data and enables non computational scientists to perform more effective data analysis.