  Electricity price forecasts play a crucial role in making key business
decisions within the electricity markets. A focal point in this domain are
probabilistic predictions, which delineate future price values in a more
comprehensive manner than simple point forecasts. The golden standard in
probabilistic approaches to predict energy prices is the Quantile Regression
Averaging (QRA) method. In this paper, we present a Python package that
encompasses the implementation of QRA, along with modifications of this
approach that have appeared in the literature over the past few years. The
proposed package also facilitates the acquisition and preparation of data
related to electricity markets, as well as the evaluation of model predictions.
