don't override Base.conv, which no longer exists

Author: awllee

src/KernelDensity.jl

@@ -8,7 +8,6 @@ using Distributions
 using Optim
 using Interpolations
 
-import Base: conv
 import StatsBase: RealVector, RealMatrix
 import Distributions: twoπ, pdf
 import FFTW: rfft, irfft

test/bivariate.jl

@@ -32,7 +32,7 @@ for X in ([0.0], [0.0,0.0], [0.0,0.5], [-0.5:0.1:0.5;])
         @test all(k1.density .>= 0.0)
         @test sum(k1.density)*step(k1.x)*step(k1.y) ≈ 1.0
 
-        k2 = conv(k1,kernel_dist(Tuple{D,D}, (0.1,0.1)))
+        k2 = KernelDensity.conv(k1,kernel_dist(Tuple{D,D}, (0.1,0.1)))
         @test isa(k2,BivariateKDE)
         @test size(k2.density) == (length(k2.x), length(k2.y))
         @test all(k2.density .>= 0.0)

test/univariate.jl

@@ -34,7 +34,7 @@ for X in ([0.0], [0.0,0.0], [0.0,0.5], [-0.5:0.1:0.5;])
         @test all(k1.density .>= 0.0)
         @test sum(k1.density)*step(k1.x) ≈ 1.0
 
-        k2 = conv(k1,kernel_dist(D,0.1))
+        k2 = KernelDensity.conv(k1,kernel_dist(D,0.1))
         @test isa(k2,UnivariateKDE)
         @test length(k2.density) == length(k2.x)
         @test all(k2.density .>= 0.0)