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‎README.md‎

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@@ -13,6 +13,7 @@ We propose a new framework of LightGBM that predicts the entire conditional dist
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:white_check_mark: LightGBMLSS is available in Python. <br/>
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## News
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:boom: [2023-06-15] LightGBMLSS now supports Zero-Inflated and Zero-Adjusted Distributions. <br/>
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:boom: [2023-05-26] Release of v0.2.1. See the [release notes](https://github.com/StatMixedML/LightGBMLSS/releases) for an overview. <br/>
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:boom: [2023-05-23] Release of v0.2.0. See the [release notes](https://github.com/StatMixedML/LightGBMLSS/releases) for an overview. <br/>
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:boom: [2022-01-05] LightGBMLSS now supports estimating the full predictive distribution via [Expectile Regression](https://epub.ub.uni-muenchen.de/31542/1/1471082x14561155.pdf). <br/>
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## Available Distributions
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LightGBMLSS currently supports the following [PyTorch distributions](https://pytorch.org/docs/stable/distributions.html).
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| Distribution | Usage |Type | Support | Number of Parameters |
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| :----------------------------------------------------------------------------------------------------------------------------------: |:------------------------: |:-------------------------------------: | :-----------------------------: | :-----------------------------: |
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| [Beta](https://pytorch.org/docs/stable/distributions.html#beta) | `Beta()` | Continuous <br /> (Univariate) | $y \in (0, 1)$ | 2 |
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| [Cauchy](https://pytorch.org/docs/stable/distributions.html#cauchy) | `Cauchy()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 2 |
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| [Expectile](https://epub.ub.uni-muenchen.de/31542/1/1471082x14561155.pdf) | `Expectile()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | Number of expectiles |
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| [Gamma](https://pytorch.org/docs/stable/distributions.html#gamma) | `Gamma()` | Continuous <br /> (Univariate) | $y \in (0, \infty)$ | 2 |
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| [Gaussian](https://pytorch.org/docs/stable/distributions.html#normal) | `Gaussian()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 2 |
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| [Gumbel](https://pytorch.org/docs/stable/distributions.html#gumbel) | `Gumbel()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 2 |
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| [Laplace](https://pytorch.org/docs/stable/distributions.html#laplace) | `Laplace()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 2 |
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| [LogNormal](https://pytorch.org/docs/stable/distributions.html#lognormal) | `LogNormal()` | Continuous <br /> (Univariate) | $y \in (0,\infty)$ | 2 |
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| [Negative Binomial](https://pytorch.org/docs/stable/distributions.html#negativebinomial) | `NegativeBinomial()` | Discrete Count <br /> (Univariate) | $y \in (0, 1, 2, 3, ...)$ | 2 |
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| [Poisson](https://pytorch.org/docs/stable/distributions.html#poisson) | `Poisson()` | Discrete Count <br /> (Univariate) | $y \in (0, 1, 2, 3, ...)$ | 1 |
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| [Student-T](https://pytorch.org/docs/stable/distributions.html#studentt) | `StudentT()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 3 |
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| [Weibull](https://pytorch.org/docs/stable/distributions.html#weibull) | `Weibull()` | Continuous <br /> (Univariate) | $y \in [0, \infty)$ | 2 |
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| Distribution | Usage |Type | Support | Number of Parameters |
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| :----------------------------------------------------------------------------------------------------------------------------------: |:------------------------: |:-------------------------------------: | :-----------------------------: | :-----------------------------: |
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| [Beta](https://pytorch.org/docs/stable/distributions.html#beta) | `Beta()` | Continuous <br /> (Univariate) | $y \in (0, 1)$ | 2 |
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| [Cauchy](https://pytorch.org/docs/stable/distributions.html#cauchy) | `Cauchy()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 2 |
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| [Expectile](https://epub.ub.uni-muenchen.de/31542/1/1471082x14561155.pdf) | `Expectile()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | Number of expectiles |
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| [Gamma](https://pytorch.org/docs/stable/distributions.html#gamma) | `Gamma()` | Continuous <br /> (Univariate) | $y \in (0, \infty)$ | 2 |
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| [Gaussian](https://pytorch.org/docs/stable/distributions.html#normal) | `Gaussian()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 2 |
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| [Gumbel](https://pytorch.org/docs/stable/distributions.html#gumbel) | `Gumbel()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 2 |
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| [Laplace](https://pytorch.org/docs/stable/distributions.html#laplace) | `Laplace()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 2 |
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| [LogNormal](https://pytorch.org/docs/stable/distributions.html#lognormal) | `LogNormal()` | Continuous <br /> (Univariate) | $y \in (0,\infty)$ | 2 |
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| [Negative Binomial](https://pytorch.org/docs/stable/distributions.html#negativebinomial) | `NegativeBinomial()` | Discrete Count <br /> (Univariate) | $y \in (0, 1, 2, 3, \ldots)$ | 2 |
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| [Poisson](https://pytorch.org/docs/stable/distributions.html#poisson) | `Poisson()` | Discrete Count <br /> (Univariate) | $y \in (0, 1, 2, 3, \ldots)$ | 1 |
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| [Student-T](https://pytorch.org/docs/stable/distributions.html#studentt) | `StudentT()` | Continuous <br /> (Univariate) | $y \in (-\infty,\infty)$ | 3 |
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| [Weibull](https://pytorch.org/docs/stable/distributions.html#weibull) | `Weibull()` | Continuous <br /> (Univariate) | $y \in [0, \infty)$ | 2 |
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| [Zero-Adjusted Beta](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py) | `ZABeta()` | Discrete-Continuous <br /> (Univariate) | $y \in [0, 1)$ | 3 |
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| [Zero-Adjusted Gamma](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py) | `ZAGamma()` | Discrete-Continuous <br /> (Univariate) | $y \in [0, \infty)$ | 3 |
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| [Zero-Adjusted LogNormal](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py) | `ZALN()` | Discrete-Continuous <br /> (Univariate) | $y \in [0, \infty)$ | 3 |
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| [Zero-Inflated Negative Binomial](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150) | `ZINB()` | Discrete-Count <br /> (Univariate) | $y \in [0, 1, 2, 3, \ldots)$ | 3 |
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| [Zero-Inflated Poisson](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121) | `ZIPoisson()` | Discrete-Count <br /> (Univariate) | $y \in [0, 1, 2, 3, \ldots)$ | 2 |
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## Some Notes
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### Stabilization

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