Last 2 columns of Table 2: Both Treatments for each Cluster, Independence working correlation
  K=20 Clusters.

     d1= tau=0
     d4=tau=.5


Va= substitue Ui with Hi * Ui in sandwich estimator of variance where 
        H[i,,]<- diag( 1/sqrt( 1- pmin(.75,diag( omegaivm ))))


tau=0
> print.sim(out$pd1, out$cid1, out$erri.d1)
[1] "Number Converged= 1000"
[1] "P-Values"
 Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 
   0.043   0.075    0.053             0.053           0.028   0.066
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
               0.046               0.022
[1] "Mean ci"
    Vm,Chi2    Vs,Chi2   Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H 
 0.06046961 0.05801953 0.06219222         0.0623549      0.07052312
    Va,Chi2 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
 0.05965625          0.06415913          0.07269504




tau=.5
> print.sim(out$pd4, out$cid4, out$erri.d4)
[1] "Number Converged= 1000"
[1] "P-Values"
 Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 
    0.33   0.074    0.055             0.055           0.029   0.062
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
               0.048               0.024
[1] "Mean ci"
   Vm,Chi2   Vs,Chi2  Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H   Va,Chi2 
 0.1145003 0.2268573 0.2431725          0.243825       0.3253714 0.2331795
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
           0.2507957           0.3350689