fix math rendering in readme

README.Rmd

@@ -26,7 +26,7 @@ knitr::opts_chunk$set(
 
 The `wildrwolf` package implements Romano-Wolf multiple-hypothesis-adjusted p-values for objects of type `fixest` and `fixest_multi` from the `fixest` package via a wild cluster bootstrap. At its current stage, the package is experimental and it is not thoroughly tested.
 
-Because the bootstrap-resampling is based on the [fwildclusterboot](https://github.com/s3alfisc/fwildclusterboot) package, `wildwyoung` is usually really fast. 
+Because the bootstrap-resampling is based on the [fwildclusterboot](https://github.com/s3alfisc/fwildclusterboot) package, `wildrwolf` is usually really fast. 
 
 The package is complementary to [wildwyoung](https://github.com/s3alfisc/wildwyoung), which implements the multiple hypothesis adjustment method following Westfall and Young (1993).
 
@@ -122,7 +122,7 @@ summary(res_rwolf)
 
 ## Performance
 
-The above procedures with `S=8` hypotheses, `N=5000` observations and `k %in% (1,2)` parameters finish each in around 3.5 seconds.
+The above procedure with `S=8` hypotheses, `N=5000` observations and `k %in% (1,2)` parameters finises in around 3.5 seconds.
 
 ```{r, warning = FALSE, message = FALSE, eval = FALSE}
 if(requireNamespace("microbenchmark")){
@@ -149,19 +149,10 @@ if(requireNamespace("microbenchmark")){
 
 We test $S=6$ hypotheses and generate data as 
 
-$$
-  Y_{i,s,g} = \beta_{0} + \beta_{1,s} D_{i} + u_{i,g} + \epsilon_{i,s}
-$$
+$$Y_{i,s,g} = \beta_{0} + \beta_{1,s} D_{i} + u_{i,g} + \epsilon_{i,s} $$
 where $D_i = 1(U_i > 0.5)$ and $U_i$ is drawn from a uniform distribution, $u_{i,g}$ is a cluster level shock with intra-cluster correlation $0.5$, and the idiosyncratic error term is drawn from a multivariate random normal distribution with mean $0_S$ and covariance matrix 
 
-$$
-\Sigma = \begin{bmatrix}
-1 & \rho & \dots & \rho  \\ 
-\rho & 1 & \dots \rho \\
-\vdots & \vdots & \ddots & \vdots \\
-\rho & \rho & \rho & 1 \\ 
-\end{bmatrix}
-$$
+$$\Sigma = \begin{bmatrix} 1 & \rho & \dots & \rho  \\ \rho & 1 & \dots \rho \\\vdots & \vdots & \ddots & \vdots \\\rho & \rho & \rho & 1 \\ \end{bmatrix}$$
 with $\rho \geq 0$. We assume that $\beta_{1,s}= 0$ for all $s$. 
 
 This experiment imposes a data generating process as in equation (9) in [Clarke, Romano and Wolf](https://docs.iza.org/dp12845.pdf), with an additional error term $u_g$ for $G=20$ clusters and intra-cluster correlation 0.5 and $N=1000$ observations.