Nothing

```
test_that("EB_CS returns three vectors", {
p = 1000
k = 7
# the prior distribution for lambda
alpha = 2
beta = 10
# lambda
lambda = rep(0, p)
pi_0 = 0.9
p_0 = floor(p*pi_0)
p_1 = p-p_0
lambda[(p_0+1):p] = rgamma(p_1, shape = alpha, rate=1/beta)
# Generate a Poisson RV
J = sapply(1:p, function(x){rpois(1, lambda[x]/2)})
X = sapply(1:p, function(x){rchisq(1, k+2*J[x])})
qq_set = seq(0.1, 0.9, 0.1)
out = EB_CS(X, k, qq=qq_set, method='LS', mixture = FALSE)
E = out$E_lambda
V = out$V_lambda
S = out$S_lambda
expect_equal(length(E), p)
expect_equal(length(V), p)
expect_equal(length(S), p)
expect_equal(sum(V>=0), p)
})
```

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