The R2CCP method can be extended to handle more complex output distributions by incorporating additional techniques to capture the nuances of these distributions. One approach could involve using more sophisticated neural network architectures, such as mixture density networks, which can model multimodal distributions effectively. By allowing the neural network to output multiple modes or heavy tails, the R2CCP method can better capture the complexity of the output distribution. Additionally, incorporating techniques like Bayesian neural networks or ensemble methods can provide a more robust estimation of the distribution, especially in cases of heavy tails or multiple modes. These methods can help improve the flexibility and adaptability of the R2CCP method to handle a wider range of output distributions.