Simplify ExKurtosis Sig

Remove mean and std from the input to ExKurtosis.

stat.go

@@ -265,25 +265,29 @@ func Entropy(p []float64) float64 {
 // the normal distribution is zero.
 // If weights is nil then all of the weights are 1. If weights is not nil, then
 // len(x) must equal len(weights).
-func ExKurtosis(x []float64, mean, stdev float64, weights []float64) float64 {
+func ExKurtosis(x, weights []float64) float64 {
+
+	// This is a three pass algorithm.  There are one pass algorithms but I am
+	// not aware of a two pass one, and I'm not sure about the relative stability
+	// of the one pass vs three pass versions.
+
+	u, std := MeanStdDev(x, weights)
 	if weights == nil {
 		var e float64
 		for _, v := range x {
-			z := (v - mean) / stdev
+			z := (v - u) / std
 			e += z * z * z * z
 		}
 		mul, offset := kurtosisCorrection(float64(len(x)))
 		return e*mul - offset
 	}
-	if len(x) != len(weights) {
-		panic("stat: slice length mismatch")
-	}
+
 	var (
 		e          float64
 		sumWeights float64
 	)
 	for i, v := range x {
-		z := (v - mean) / stdev
+		z := (v - u) / std
 		e += weights[i] * z * z * z * z
 		sumWeights += weights[i]
 	}
@@ -768,12 +772,12 @@ func Quantile(p float64, c CumulantKind, x, weights []float64) float64 {
 // Skew computes the skewness of the sample data.
 // If weights is nil then all of the weights are 1. If weights is not nil, then
 // len(x) must equal len(weights).
-func Skew(x []float64, weights []float64) float64 {
-	
+func Skew(x, weights []float64) float64 {
+
 	// this is a three-pass approach.  There are one pass approaches, but
-	// I don't know of a two-pass one, or the numerical stability of the 
+	// I don't know of a two-pass one, or the numerical stability of the
 	// one vs three pass version.
-	
+
 	u, std := MeanStdDev(x, weights)
 	if weights == nil {
 		var s float64