diff --git a/docs/content/DescriptiveStatistics.fsx b/docs/content/DescriptiveStatistics.fsx
index 1757b3c4..9a721c3e 100644
--- a/docs/content/DescriptiveStatistics.fsx
+++ b/docs/content/DescriptiveStatistics.fsx
@@ -205,7 +205,7 @@ Statistics.OrderStatistic(whiteNoise, 1)
 Statistics.OrderStatistic(whiteNoise, 1000)
 // [fsi:val it : float = 16.65183566]
 
-let os = Statistics.orderStatisticF whiteNoise
+let os = Statistics.orderStatisticFunc whiteNoise
 os 250
 // [fsi:val it : float = 8.645491746]
 os 500
@@ -424,7 +424,7 @@ Generate.LinearSpacedMap(20, start=3.0, stop=17.0, map=ecdf)
 // [fsi:    [|0.0; 0.001; 0.002; 0.005; 0.022; 0.05; 0.094; 0.172; 0.278; 0.423; 0.555; ]
 // [fsi:      0.705; 0.843; 0.921; 0.944; 0.983; 0.992; 0.997; 0.999; 1.0|] ]
 
-let eicdf = Statistics.empiricalInvCdfF whiteNoise
+let eicdf = Statistics.empiricalInvCDFFunc whiteNoise
 [ for tau in 0.0..0.05..1.0 -> eicdf tau ]
 // [fsi:val it : float [] =]
 // [fsi:    [3.633070184; 6.682142043; 7.520000817; 8.040513497; 8.347587493; ]
diff --git a/src/FSharp/Fit.fs b/src/FSharp/Fit.fs
index 2fd45512..f2c604f8 100644
--- a/src/FSharp/Fit.fs
+++ b/src/FSharp/Fit.fs
@@ -45,7 +45,7 @@ module Fit =
 
     /// Least-Squares fitting the points (x,y) to a line y : x -> a+b*x,
     /// returning a function y' for the best fitting line.
-    let lineF x y = Fit.LineFunc(x,y) |> tofs
+    let lineFunc x y = Fit.LineFunc(x,y) |> tofs
 
 
     /// Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 + ... + pk*x^k,
@@ -54,7 +54,7 @@ module Fit =
 
     /// Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 + ... + pk*x^k,
     /// returning a function y' for the best fitting polynomial.
-    let polynomialF order x y = Fit.PolynomialFunc(x,y,order) |> tofs
+    let polynomialFunc order x y = Fit.PolynomialFunc(x,y,order) |> tofs
 
 
     /// Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
@@ -68,6 +68,6 @@ module Fit =
 
     /// Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
     /// returning a function y' for the best fitting combination.
-    let linearF functions x y =
+    let linearFunc functions x y =
         let parts = linear functions x y |> List.zip functions
         in fun z -> parts |> List.fold (fun s (f,p) -> s+p*(f z)) 0.0
diff --git a/src/FSharp/Statistics.fs b/src/FSharp/Statistics.fs
index c60b93ab..dbeb88d9 100644
--- a/src/FSharp/Statistics.fs
+++ b/src/FSharp/Statistics.fs
@@ -37,11 +37,11 @@ module Statistics =
 
     let private tofs (f:Func<_,_>) = fun a -> f.Invoke(a)
 
-    let quantileF (data : float seq) = Statistics.QuantileFunc(data) |> tofs
-    let quantileCustomF (data : float seq) definition = Statistics.QuantileCustomFunc(data, definition) |> tofs
-    let percentileF (data : float seq) = Statistics.PercentileFunc(data) |> tofs
-    let orderStatisticF (data : float seq) = Statistics.OrderStatisticFunc(data) |> tofs
-    let quantileRankF (data : float seq) = Statistics.QuantileRankFunc(data) |> tofs
-    let quantileRankCustomF (data : float seq) definition = Statistics.QuantileRankFunc(data, definition) |> tofs
-    let empiricalCdfF (data : float seq) = Statistics.EmpiricalCDFFunc(data) |> tofs
-    let empiricalInvCdfF (data : float seq) = Statistics.EmpiricalInvCDFFunc(data) |> tofs
+    let quantileFunc (data : float seq) = Statistics.QuantileFunc(data) |> tofs
+    let quantileCustomFunc (data : float seq) definition = Statistics.QuantileCustomFunc(data, definition) |> tofs
+    let percentileFunc (data : float seq) = Statistics.PercentileFunc(data) |> tofs
+    let orderStatisticFunc (data : float seq) = Statistics.OrderStatisticFunc(data) |> tofs
+    let quantileRankFunc (data : float seq) = Statistics.QuantileRankFunc(data) |> tofs
+    let quantileRankCustomFunc (data : float seq) definition = Statistics.QuantileRankFunc(data, definition) |> tofs
+    let empiricalCDFFunc (data : float seq) = Statistics.EmpiricalCDFFunc(data) |> tofs
+    let empiricalInvCDFFunc (data : float seq) = Statistics.EmpiricalInvCDFFunc(data) |> tofs
diff --git a/src/FSharpUnitTests/FitTests.fs b/src/FSharpUnitTests/FitTests.fs
index 651c6e4f..7bff82c8 100644
--- a/src/FSharpUnitTests/FitTests.fs
+++ b/src/FSharpUnitTests/FitTests.fs
@@ -18,7 +18,7 @@ module FitTests =
         a |> should (equalWithin 1.0e-12) 4.0
         b |> should (equalWithin 1.0e-12) -1.5
 
-        let fres = Fit.lineF x y
+        let fres = Fit.lineFunc x y
         in x |> Array.iter (fun x -> fres x |> should (equalWithin 1.0e-12) (f x))
 
     [<Test>]
@@ -34,5 +34,5 @@ module FitTests =
         (x,y) ||> Fit.linear [(fun _ -> 1.0); (Math.Sin); (Math.Cos) ]
         |> should (equalWithin 1.0e-4) [ -0.287476; 4.02159; -1.46962 ]
 
-        let fres = Fit.linearF [(fun z -> 1.0); (fun z -> Math.Sin(z)); (fun z -> Math.Cos(z))] x y
+        let fres = Fit.linearFunc [(fun z -> 1.0); (fun z -> Math.Sin(z)); (fun z -> Math.Cos(z))] x y
         in x |> Array.iter (fun x -> fres x |> should (equalWithin 1.0e-4) (4.02159*Math.Sin(x) - 1.46962*Math.Cos(x) - 0.287476))