Update README.md

README.md

@@ -56,7 +56,7 @@ XGBoostLSS currently supports the following distributions.
 | [Multivariate Normal (Low-Rank)](https://pytorch.org/docs/stable/distributions.html#lowrankmultivariatenormal)                       | `MVN_LoRa(D, rank)`       | Continuous <br /> (Multivariate)           | $y_{D} \in (-\infty,\infty)$    | D(2+rank)                       |
 | [Multivariate Student-T](https://docs.pyro.ai/en/stable/distributions.html#multivariatestudentt)                                     | `MVT(D)`                  | Continuous <br /> (Multivariate)           | $y_{D} \in (-\infty,\infty)$    | 1 + D(D + 3)/2                  |
 | [Negative Binomial](https://pytorch.org/docs/stable/distributions.html#negativebinomial)                                             | `NegativeBinomial()`      | Discrete Count <br /> (Univariate)         | $y \in (0, 1, 2, 3, \ldots)$    | 2                               |
-| [Spline Flow](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline)                                | `SplineFlow()`            | Continuous \& Discrete Count <br /> (Univariate)   | $y \in (-\infty,\infty)$ <br /> $y \in [0, 1]$  <br  /> $y \in (0, \infty)$  <br  /> $y \in (0, 1, 2, 3, \ldots)$ | 2xcount_bins + (count_bins-1) (order=quadratic)  <br  />  3xcount_bins + (count_bins-1) (order=linear)                            |
+| [Spline Flow](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline)                                | `SplineFlow()`            | Continuous \& Discrete Count <br /> (Univariate)   | $y \in (-\infty,\infty)$ <br /> $y \in [0, 1]$  <br  /> $y \in [0, \infty)$  <br  /> $y \in (0, 1, 2, 3, \ldots)$ | 2xcount_bins + (count_bins-1) (order=quadratic)  <br  />  3xcount_bins + (count_bins-1) (order=linear)                            |
 | [Poisson](https://pytorch.org/docs/stable/distributions.html#poisson)                                                                | `Poisson()`               | Discrete Count <br /> (Univariate)         | $y \in (0, 1, 2, 3, \ldots)$    | 1                               |
 | [Student-T](https://pytorch.org/docs/stable/distributions.html#studentt)                                                             | `StudentT()`              | Continuous <br /> (Univariate)             | $y \in (-\infty,\infty)$        | 3                               |
 | [Weibull](https://pytorch.org/docs/stable/distributions.html#weibull)                                                                | `Weibull()`               | Continuous <br /> (Univariate)             | $y \in [0, \infty)$             | 2                               |