function R = testOS(B, data, pnorm, alpha)
%Peforms the one-sample test. Set pnorm = inf for the
%sup-norm statistic
%INPUTS:
% *B - number of Monte-Carlo samples to use to estimate the null distribution.
% *data - r x d matrix. Each observation is dx1 vector and there are r
% vectors
% *pnorm - norm of the test (e.g. pnorm = inf)
% *alpha - size of the test (e.g. alpha = .05)
%OUTPUTS:
% *R - indicator for rejection of the null hypothesis, mean vector equal to
% zero



%Step 1. Compute test stat
T = norm_stat(data, pnorm);

%Step 2. Estimate the Covariance Matrix
sigma = covshrinkKPM(data, 1);

%%Step 3. Approximate the null distribution
nullv = null_distAC(B,sigma,pnorm);

%Step 4. Decide to reject or not 
tail = 0;

[CV R] = BSHTcv(T, nullv, alpha, tail);