diff --git a/src/correlation_analysis.rs b/src/correlation_analysis.rs
index fea772c..f7cff7d 100644
--- a/src/correlation_analysis.rs
+++ b/src/correlation_analysis.rs
@@ -8,11 +8,14 @@ use super::statistics;
 // Method for the Analysis of Simulated and Parallel Tempering Simulations, JCTC"
 pub fn statistical_ineff(timeseries: &[f64]) -> f64 {
     let n = timeseries.len();
-    let autocorr = autocorrelation(timeseries);
-
+    let mean = statistics::mean(timeseries);
+    let d_mean = timeseries.iter().map(|x| x-mean).collect::<Vec<f64>>();
+    let cov = statistics::autocov(timeseries);
+    
     let mut g = 1.0;
     for t in 1..(n-1) {
-        let c = autocorr[t-1];
+        let tmp = d_mean[0..n-t].iter().zip(d_mean[t..n].iter()).map(|(x,y)| x*y);
+        let c = tmp.map(|x| x+x).sum::<f64>() / (2.0 * (n as f64 - t as f64)*cov);
         if c <= 0.0 {
             break;
         }
@@ -25,22 +28,6 @@ pub fn statistical_ineff(timeseries: &[f64]) -> f64 {
     }
 }
 
-// calculates the autocorrelation of a simeseries
-fn autocorrelation(timeseries: &[f64]) -> Vec<f64> {
-    let n = timeseries.len();
-    let mean = statistics::mean(timeseries);
-    let d_mean = timeseries.iter().map(|x| x-mean).collect::<Vec<f64>>();
-    let cov = statistics::autocov(timeseries);
-
-    let mut autocorr = Vec::new();
-    for t in 1..(n-1) {
-        let tmp = d_mean[0..n-t].iter().zip(d_mean[t..n].iter()).map(|(x, y)| x*y);
-        let c: f64 = tmp.map(|x| x+x).sum::<f64>() / (2.0 * (n as f64-t as f64)*cov);
-        autocorr.push(c);
-    }
-    autocorr
-}
-
 // The autocorrelation time of a timeseries can be deduced from the
 // `statistical_ineff` by (g-1)/2.0 
 pub fn autocorrelation_time(g: f64) -> f64 {
@@ -65,23 +52,6 @@ mod tests {
 
     }
 
-    #[test]
-    fn autocorrelation() {
-        let timeseries = read_timeseries("example/1d_cyclic/COLVAR-2.5.xvg");
-        let autocorr = super::autocorrelation(&timeseries);
-        let expected = [
-            0.691_900_865_597_914_3, 0.533_171_939_967_135_5,
-            0.204_726_209_564_635_89, -0.002_850_876_458_920_514,
-            -0.138_428_500_779_381_46, -0.265_292_355_297_323_2,
-            -0.314_271_982_722_353_85, -0.261_750_515_155_769_3,
-            -0.205_948_647_302_903_38, -0.133_100_190_918_118_12,
-            -0.188_756_890_119_342_6, -0.194_493_662_542_493_3,
-            -0.196_359_967_318_946_1, -0.113_915_878_338_380_03];
-        for (actual, expected) in autocorr.iter().zip(expected.iter()) {
-            assert!((actual-expected).abs() < 0.001);
-        }
-    }
-
     #[test]
     fn statistical_ineff() {
         let timeseries = read_timeseries("example/1d_cyclic/COLVAR-2.5.xvg");
@@ -99,4 +69,4 @@ mod tests {
         assert!((tau - 1.430).abs() < 0.001)
     }
 
-}
\ No newline at end of file
+}