The lmerTest package extends the lmerMod class from the lmer4 package by adding p-values for fixed effects tests using the anova and summary functions. It implements Satterthwaite's method for approximating degrees of freedom for t and F tests, as well as the Kenward-Roger approximation for denominator degrees of freedom. The package also provides Type I–III ANOVA tables and tools for backward elimination of non-significant effects, calculation of least squares means, and multiple comparison tests. The package includes functions for generating ANOVA tables, performing F tests, and calculating p-values using Satterthwaite's and Kenward-Roger's approximations. The lmerTest package is designed to be user-friendly, with methods wrapped into the anova and summary functions for ease of use. The package also provides a step function for automated model simplification and a step-down approach for model building. The computational efficiency of the Satterthwaite's method is generally better than the Kenward-Roger's method, although the latter provides more accurate p-values. The package is useful for analyzing linear mixed effects models in a variety of fields, including sensory and consumer studies. The lmerTest package provides a flexible and powerful tool for statistical analysis of mixed effects models in R.The lmerTest package extends the lmerMod class from the lmer4 package by adding p-values for fixed effects tests using the anova and summary functions. It implements Satterthwaite's method for approximating degrees of freedom for t and F tests, as well as the Kenward-Roger approximation for denominator degrees of freedom. The package also provides Type I–III ANOVA tables and tools for backward elimination of non-significant effects, calculation of least squares means, and multiple comparison tests. The package includes functions for generating ANOVA tables, performing F tests, and calculating p-values using Satterthwaite's and Kenward-Roger's approximations. The lmerTest package is designed to be user-friendly, with methods wrapped into the anova and summary functions for ease of use. The package also provides a step function for automated model simplification and a step-down approach for model building. The computational efficiency of the Satterthwaite's method is generally better than the Kenward-Roger's method, although the latter provides more accurate p-values. The package is useful for analyzing linear mixed effects models in a variety of fields, including sensory and consumer studies. The lmerTest package provides a flexible and powerful tool for statistical analysis of mixed effects models in R.