Added Kernel Density Estimation methods (KDEyML, KDEyCS, KDEyHD) as proposed in the paper: Moreo, A., González, P., & del Coz, J. J. Kernel Density Estimation for Multiclass Quantification. arXiv preprint arXiv:2401.00490, 2024 Substantial internal refactor: aggregative methods now inherit a pattern by which the fit method consists of: fitting the classifier and returning the representations of the training instances (typically the posterior probabilities, the label predictions, or the classifier scores, and typically obtained through kFCV). fitting an aggregation function The function implemented in step a) is inherited from the super class. Each new aggregative method now has to implement only the "aggregative_fit" of step b). This pattern was already implemented for the prediction (thus allowing evaluation functions to be performed very quicky), and is now available also for training. The main benefit is that model selection now can nestle the training of quantifiers in two levels: one for the classifier, and another for the aggregation function. As a result, a method with a param grid of 10 combinations for the classifier and 10 combinations for the quantifier, now implies 10 trainings of the classifier + 1010 trainings of the aggregation function (this is typically much faster than the classifier training), whereas in versions 1000 instances - >2 classes - classification datasets - Python API available New IFCB (plankton) dataset added (thanks to Pablo González). See qp.datasets.fetch_IFCB. Added new evaluation measures NAE, NRAE (thanks to Andrea Esuli) Added new meta method "MedianEstimator"; an ensemble of binary base quantifiers that receives as input a dictionary of hyperparameters that will explore exhaustively, fitting and generating predictions for each combination of hyperparameters, and that returns, as the prevalence estimates, the median across all predictions. Added "custom_protocol.py" example. New API documentation template.