From 0a59dcd17ccc64164b43ef8065f5732d3f82d271 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20M=C3=A4rz?=
 <41187941+StatMixedML@users.noreply.github.com>
Date: Wed, 9 Aug 2023 09:34:40 +0200
Subject: [PATCH] Update README.md

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 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 07ebbff4..554534f1 100644
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@@ -28,6 +28,7 @@ We propose a new framework of XGBoost that predicts the entire conditional distr
 :white_check_mark: Estimation of all distributional parameters. <br/>
 :white_check_mark: Multi-target regression allows modelling of multivariate responses and their dependencies. <br/>
 :white_check_mark: Normalizing Flows allow modelling of complex and multi-modal distributions. <br/>
+:white_check_mark: Zero-Adjusted and Zero-Inflated Distributions for modelling excess of zeros in the data. <br/>
 :white_check_mark: Automatic derivation of Gradients and Hessian of all distributional parameters using [PyTorch](https://pytorch.org/docs/stable/autograd.html). <br/>
 :white_check_mark: Automated hyper-parameter search, including pruning, is done via [Optuna](https://optuna.org/). <br/>
 :white_check_mark: The output of XGBoostLSS is explained using [SHapley Additive exPlanations](https://github.com/dsgibbons/shap). <br/>