alpha
The amount to scale the proposal, i.e,
Xnew=Xcur+alpha*Xproposed where Xproposed is generated from a mean-zero
multivariate normal. Varying alpha varies the acceptance rate.
covar
An optional covariance matrix which can be used to improve
the efficiency of sampling. The lower Cholesky decomposition of this
matrix is used to transform the parameter space. If the posterior is
approximately multivariate normal and covar approximates the
covariance, then the transformed parameter space will be close to
multivariate standard normal. In this case the algorithm will be more
efficient, but there will be overhead in the matrix calculations which
need to be done at each step. The default of NULL specifies to not do
this transformation.