Run the hierarchical mcmc model to infer priors

```
metrop_run(
lbf_mat,
nsnps,
covar_vec,
covar = FALSE,
nits = 10000L,
thin = 1L,
alpha_mean = -10,
alpha_sd = 0.5,
beta_shape = 2,
beta_scale = 2,
gamma_shape = 2,
gamma_scale = 2
)
```

- lbf_mat
matrix of log bayes factors: lBF.Ha and lBF.Hc

- nsnps
number of snps

- covar_vec
Vector of the covariate

- covar
logical: Should the covariate inflormation be used? default: False

- nits
Number of iterations run in mcmc

- thin
thinning

- alpha_mean
prior for the mean of alpha

- alpha_sd
prior for the standard deviation of alpha

- beta_shape
prior for the shape (gamma distibution) of beta

- beta_scale
prior for the scale of beta

- gamma_shape
prior for the shape (gamma distibution) of gamma

- gamma_scale
prior for the scale of gamma

named list of log likelihood (ll) and parameters: alpha, beta and gamma