diff --git a/README.md b/README.md
index 358ffb9..77a51e2 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,6 @@
-# `gmwmx2` Overview 
+# `gmwmx2` Overview
+
+
The `gmwmx2` `R` package implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in [Voirol, L., Xu, H., Zhang, Y., Insolia, L., Molinari, R. and Guerrier, S. (2024)](https://arxiv.org/abs/2409.05160).
The GMWMX estimator is a computationally efficient estimator to estimate large scale regression problems with complex dependence structure in presence of missing data.
diff --git a/vignettes/fit_model.Rmd b/vignettes/fit_model.Rmd
index f39a66d..f9ee122 100644
--- a/vignettes/fit_model.Rmd
+++ b/vignettes/fit_model.Rmd
@@ -139,7 +139,7 @@ $$
$$
When the argument `stochastic_model` is set to `"wn + pl"`, the stochastic model considered includes both white noise and colored noise with the specified above autocovariance structure. The model is therefore stationary and the parameters estimated are: $\sigma^2_{W N}$, $\kappa$ (constrained to be greater than $-1$) and $\sigma^2_{P L}$.
-When the argument `stochastic_model` is set to `"wn + fl"`, the stochastic model considered includes both white noise and flicker noise (not stationary power-law noise with spectral index $\kappa=-1$) where the variance covariance of the flicker noise $\omega$ is obtained as follows (see e.g., [@bos2008fast]):
+When the argument `stochastic_model` is set to `"wn + fl"`, the stochastic model considered includes both white noise and flicker noise (not stationary power-law noise with spectral index $\kappa=-1$) where the variance covariance of the flicker noise $\omega$ is obtained as follows (see e.g., [@bos2008fast]):
$$
\operatorname{Cov}(\omega) = \sigma^2_{F L}\mathbf{U}^T \mathbf{U}