Artificial Neural Networks (ANNs) are powerful models in representation learning, known for their high prediction performance. However, their predictions often lack proper calibration, leading to unreliable uncertainty estimation. Various methods have been proposed to address this issue, but implementing and selecting the appropriate technique can be challenging. Therefore, this work presents an R package that offers a computational implementation of a quantile-based post-processing technique for recalibration. We hope this package will encourage researchers to pursue reliable uncertainty estimates from their models. The current version of the package provides functions to recalibrate Gaussian models, such as neural networks adjusted with the MSE loss function. The method leverages information from cumulative probabilities, enabling the generation of Monte Carlo samples from the recalibrated predictive distribution and performing recalibration locally and globally (https://arxiv.org/abs/2403.05756). The so-called recalibratriNN package also includes diagnostic functions to visualize miscalibration. It is readily available on GitHub (https://cmusso86.github.io/recalibratiNN/).