Analysis of such studies can be successfully performed using nonparametric regression models. In the nonparametric regression framework, issues of interest include the so‐called factor‐by‐curve interaction, where the effect of a continuous covariate on response varies across groups defined by levels of a categorical variable. This study sought to compare regression curves and their derivatives that may vary across groups defined by different experimental conditions. For this purpose, we propose the use of local linear kernel smoothers. This study introduces a software application for R which performs inference in a nonparametric regression model. It describes the capabilities of the program for estimating these models (and their derivatives) and for drawing different regression curves by factor levels. The main feature of the package is its ability to draw inferences about critical points, such as maxima or change points linked to the derivative curves. Bootstrap methods were implemented to draw inferences from the derivative curves, and binning techniques were applied to speed up computation in the estimation and testing processes. The software is illustrated using biological data.