Fix typo

README.md

@@ -21,7 +21,7 @@ The main idea of distributional regression (or regression beyond the mean, multi
 
 $$g_k(\theta_k) = \eta_k = X_k\beta_k$$
 
-where $g_k(\cdot)$ is a link function, which ensures that the predicted distribution parameters are in a sensible range (we don't want, e.g. negative standard deviations), and $\eta_k$ is the predictor. For the Gaussian case, this would imply that we have two regression equations, one for the mean (location) and one for the standard deviation (scale) parameters. Distributions other than the normal distribution are possible, and we have already implemented them, e.g., Student's $ t$ distribution and Johnson's $S_U$ distribution. If you are interested in another distribution, please open an Issue.
+where $g_k(\cdot)$ is a link function, which ensures that the predicted distribution parameters are in a sensible range (we don't want, e.g. negative standard deviations), and $\eta_k$ is the predictor. For the Gaussian case, this would imply that we have two regression equations, one for the mean (location) and one for the standard deviation (scale) parameters. Distributions other than the normal distribution are possible, and we have already implemented them, e.g., Student's $t$-distribution and Johnson's $S_U$ distribution. If you are interested in another distribution, please open an Issue.
 
 This allows us to specify very flexible models that consider the conditional behaviour of the variable's volatility, skewness and tail behaviour. A simple example for electricity markets is wind forecasts, which are skewed depending on the production level - intuitively, there is a higher risk of having lower production if the production level is already high since it cannot go much higher than "full load" and if, the turbines might cut-off. Modelling these conditional probabilistic behaviours is the key strength of distributional regression models.
 
@@ -109,4 +109,4 @@ To get started, just create a fork and get going. We will modularize the code ov
 2) Install the necessary dependencies from the `requirements.txt` using `conda create --name <env> --file requirements.txt`. 
 3) Run `python3 -m build` to build the wheel.
 4) Run `pip install dist/rolch-0.1.0-py3-none-any.whl` with the accurate version. If necessary, append `--force-reinstall`
-5) Enjoy.O
+5) Enjoy.O