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diff --git a/test/helper.R b/test/helper.R
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+#' Generate a sample of (X,Y) of size n with default values
+#'
+#' @param n sample size
+#' @param p number of covariates
+#' @param m size of the response
+#' @param k number of clusters
+#'
+#' @return list with X and Y
+#'
+generateXYdefault = function(n, p, m, k)
+{
+	meanX = rep(0, p)
+	covX = diag(p)
+	covY = array(dim=c(m,m,k))
+	for(r in 1:k)
+		covY[,,r] = diag(m)
+	π = rep(1./k,k)
+	#initialize beta to a random number of non-zero random value
+	β = array(0, dim=c(p,m,k))
+	for (j in 1:p)
+	{
+		nonZeroCount = sample(1:m, 1)
+		β[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
+	}
+
+	sample_IO = generateXY(n, π, meanX, β, covX, covY)
+	return (list(X=sample_IO$X,Y=sample_IO$Y))
+}
+
+#' Initialize the parameters in a basic way (zero for the conditional mean, uniform for
+#' weights, identity for covariance matrices, and uniformly distributed for the
+#' clustering)
+#'
+#' @param n sample size
+#' @param p number of covariates
+#' @param m size of the response
+#' @param k number of clusters
+#'
+#' @return list with phiInit, rhoInit,piInit,gamInit
+#'
+basicInitParameters = function(n,p,m,k)
+{
+	phiInit = array(0, dim=c(p,m,k))
+
+	piInit = (1./k)*rep(1,k)
+
+	rhoInit = array(dim=c(m,m,k))
+	for (i in 1:k)
+		rhoInit[,,i] = diag(m)
+
+	gamInit = 0.1 * matrix(1, nrow=n, ncol=k)
+	R = sample(1:k, n, replace=TRUE)
+	for (i in 1:n)
+		gamInit[i,R[i]] = 0.9
+	gamInit = gamInit/sum(gamInit[1,])
+
+	return (list("phiInit"=phiInit, "rhoInit"=rhoInit, "piInit"=piInit, "gamInit"=gamInit))
+}