Conformal prediction constitutes a flexible family of methods for predictive uncertainty quantification, which has recently drawn significant attention in the statistical literature because of its distribution-free, finite-sample, and model-agnostic coverage guarantees. While numerous methodological advances have extended the original conformal framework to broader settings, the availability of related software in R remains limited. In this paper, we present a review of several conformal prediction methods and exemplify the use of two available R packages, with a focus on regression for continuous responses given covariates and its extensions to time series forecasting. We further identify current limitations and outline opportunities for future improvements.