Dispatch non Float-arrays to Base methods

src/NaNMath.jl

@@ -66,6 +66,8 @@ function sum{T<:AbstractFloat}(x::AbstractArray{T})
     return result
 end
 
+sum(x) = Base.sum(x)
+
 """
 NaNMath.maximum(A)
 
@@ -93,6 +95,8 @@ function maximum{T<:AbstractFloat}(x::AbstractArray{T})
     return result
 end
 
+maximum(x) = Base.maximum(x)
+
 """
 NaNMath.minimum(A)
 
@@ -120,6 +124,8 @@ function minimum{T<:AbstractFloat}(x::AbstractArray{T})
     return result
 end
 
+minimum(x) = Base.minimum(x)
+
 """
 NaNMath.extrema(A)
 
@@ -149,6 +155,8 @@ function extrema{T<:AbstractFloat}(x::AbstractArray{T})
     return resultmin, resultmax
 end
 
+extrema(x) = Base.extrema(x)
+
 """
 NaNMath.mean(A)
 
@@ -168,6 +176,8 @@ function mean{T<:AbstractFloat}(x::AbstractArray{T})
     return mean_count(x)[1]
 end
 
+mean(x) = Base.mean(x)
+
 """
 Returns a tuple of the arithmetic mean of all elements in the array, ignoring NaN's,
 and the number of non-NaN values in the array.
@@ -209,7 +219,7 @@ using NaNMath as nm
 nm.var([1., 2., NaN]) # result: 0.5
 ```
 """
-function var{T<:AbstractFloat}(x::Vector{T})
+function var{T<:AbstractFloat}(x::AbstractVector{T})
     mean_val, n = mean_count(x)
     if !isnan(mean_val)
         sum_square = zero(eltype(x))
@@ -224,6 +234,8 @@ function var{T<:AbstractFloat}(x::Vector{T})
     end
 end
 
+var(x) = Base.var(x)
+
 """
 NaNMath.std(A)
 
@@ -243,9 +255,10 @@ using NaNMath as nm
 nm.std([1., 2., NaN]) # result: 0.7071067811865476
 ```
 """
-function std{T<:AbstractFloat}(x::Vector{T})
+function std{T<:AbstractFloat}(x::AbstractVector{T})
     return sqrt(var(x))
 end
+std(x) = Base.std(x)
 
 """
     NaNMath.min(x, y)