PostForecasts.jl: A Julia package for probabilistic forecasting by postprocessing point predictions

Postprocessing of point predictions is a relatively simple and efficient way to compute probabilistic forecasts, which are the basis of uncertainty assessment for decision support and risk management. The PostForecasts.jl package in Julia provides types and functions to easily convert point forecasts into probabilistic ones using Historical Simulation, Conformal Prediction, Isotonic Distributional Regression, and variants of Quantile Regression Averaging. By leveraging the developments in the point forecasting literature, it offers a set of easy-to-use, computationally undemanding, and robust tools to derive predictive distributions.