fix 0.7 deprecation

.travis.yml

@@ -3,7 +3,7 @@ os:
     - osx
     - linux
 julia:
-    - 0.5
+    - 1.0
     - nightly
 matrix:
   allow_failures:

README.md

@@ -1,12 +1,7 @@
 # KernelEstimator
 
-[![KernelEstimator](http://pkg.julialang.org/badges/KernelEstimator_0.5.svg)](http://pkg.julialang.org/?pkg=KernelEstimator)
-[![KernelEstimator](http://pkg.julialang.org/badges/KernelEstimator_0.6.svg)](http://pkg.julialang.org/?pkg=KernelEstimator)
-
 Linux: [![Build Status](https://travis-ci.org/panlanfeng/KernelEstimator.jl.svg?branch=master)](https://travis-ci.org/panlanfeng/KernelEstimator.jl)
 
-[![Coverage Status](https://coveralls.io/repos/panlanfeng/KernelEstimator.jl/badge.svg?branch=master)](https://coveralls.io/r/panlanfeng/KernelEstimator.jl?branch=master)
-
 
 The Julia package for nonparametric kernel density estimate and regression. This package currently includes univariate kernel density estimate, local constant regression (Nadaraya-Watson regression) and local linear regression. It can also compute the Bootstrap confidence band [4].
 

REQUIRE

@@ -1,7 +1,6 @@
-julia 0.5
+julia 0.7
 Distributions
-Optim 0.5.0
+Optim
 StatsBase
-Cubature 1.2.0
-StatsFuns
-Yeppp
+HCubature
+SpecialFunctions

src/KernelEstimator.jl

@@ -1,15 +1,14 @@
 VERSION >= v"0.4" && __precompile__()
 
-module KernelEstimator 
+module KernelEstimator
 using Distributions
 using Optim
-using Yeppp
 using StatsBase
-using Cubature
+using HCubature
 import StatsBase: RealVector, RealMatrix
-using StatsFuns
-import StatsFuns: invsqrt2π, log2π, sqrt2, invsqrt2
-# Compat.@irrational invsqrt2π 0.398942280401432677939946 big(1.)/sqrt(big(2.)*π)
+
+using Distributions: invsqrt2π, log2π, sqrt2, invsqrt2
+using SpecialFunctions
 
 
 

src/bandwidth.jl

@@ -44,7 +44,7 @@ end
 
 #for general kernel
 function Jh(xdata::RealVector, kernel::Function, h::Real, w::Vector, n::Int, xlb::Real, xub::Real)
-    pquadrature(x->begin kernel(x, xdata,h,w,n); mean(w)^2; end, xlb, xub, maxevals=200)[1] - leaveoneout(xdata, kernel, h, w, n)
+    hquadrature(x->begin kernel(x, xdata,h,w,n); mean(w)^2; end, xlb, xub, maxevals=200)[1] - leaveoneout(xdata, kernel, h, w, n)
 end
 function leaveoneout(xdata::RealVector, kernel::Function, h::Real, w::Vector, n::Int)
 
@@ -58,7 +58,7 @@ function leaveoneout(xdata::RealVector, kernel::Function, h::Real, w::Vector, n:
 end
 #For betakernel
 function Jh(xdata::RealVector, logxdata::RealVector,log1_xdata::RealVector, kernel::Function, h::Real, w::Vector, n::Int, xlb::Real, xub::Real)
-    pquadrature(x->begin kernel(x, logxdata, log1_xdata, h,w,n); mean(w)^2; end, xlb, xub, maxevals=200)[1] - leaveoneout(xdata, logxdata, log1_xdata, kernel, h, w, n)
+    hquadrature(x->begin kernel(x, logxdata, log1_xdata, h,w,n); mean(w)^2; end, xlb, xub, maxevals=200)[1] - leaveoneout(xdata, logxdata, log1_xdata, kernel, h, w, n)
 end
 function leaveoneout(xdata::RealVector, logxdata::RealVector, log1_xdata::RealVector, kernel::Function, h::Real, w::Vector, n::Int)
 
@@ -72,7 +72,7 @@ function leaveoneout(xdata::RealVector, logxdata::RealVector, log1_xdata::RealVe
 end
 #For gammakernel
 function Jh(xdata::RealVector, logxdata::RealVector, kernel::Function, h::Real, w::Vector, n::Int, xlb::Real, xub::Real)
-    pquadrature(x->begin kernel(x, xdata, logxdata, h,w,n); mean(w)^2; end, xlb, xub, maxevals=200)[1] - leaveoneout(xdata, logxdata, kernel, h, w, n)
+    hquadrature(x->begin kernel(x, xdata, logxdata, h,w,n); mean(w)^2; end, xlb, xub, maxevals=200)[1] - leaveoneout(xdata, logxdata, kernel, h, w, n)
 end
 function leaveoneout(xdata::RealVector, logxdata::RealVector, kernel::Function, h::Real, w::Vector, n::Int)
 
@@ -104,12 +104,12 @@ function bwlscv(xdata::RealVector, kernel::Function)
         xlb = 0.0
         xub = 1.0
         hub = 0.25
-        logxdata = Yeppp.log(xdata)
-        log1_xdata = Yeppp.log(1.0 .- xdata)
+        logxdata = log.(xdata)
+        log1_xdata = log.(1.0 .- xdata)
         return Optim.minimizer(Optim.optimize(h -> Jh(xdata, logxdata, log1_xdata, kernel, h, w, n, xlb,xub), hlb, hub, iterations=200,abs_tol=h0/n^2))
     elseif kernel == gammakernel
         xlb = 0.0
-        logxdata = Yeppp.log(xdata)
+        logxdata = log.(xdata)
         return Optim.minimizer(Optim.optimize(h -> Jh(xdata, logxdata, kernel, h, w, n, xlb,xub), hlb, hub, iterations=200,abs_tol=h0/n^2))
     end
     return Optim.minimizer(Optim.optimize(h -> Jh(xdata, kernel, h, w, n, xlb,xub), hlb, hub, iterations=200,abs_tol=h0/n^2))
@@ -342,7 +342,7 @@ function bwlocalconstant(xdata::RealMatrix, ydata::RealVector, kernel::Array{Fun
     if any(h_output .<= 0.0)
         for j in 1:p
             if h_output[j] .<= 0.0
-                h_output[j] = 2.* h0[j]
+                h_output[j] = 2 .* h0[j]
             end
         end
         h_output = Optim.minimizer(Optim.optimize(h->lscvlocalconstant(xdata, ydata, kernel, h, w, n), h_output))

src/kernel.jl

@@ -41,20 +41,20 @@ function betakernel(x::Real, xdata::RealVector, h::Real, w::Vector, n::Int)
     elseif x>1-2*h
         b = rhoxb(1-x, h) - 1
     end
-    
+
     minus!(w, 1.0, xdata, n)
-    Yeppp.log!(w, w)
-    wtmp = Yeppp.log(xdata)
+    w .= log.(w)
+    wtmp = log.(xdata)
     multiply!(w, b)
     multiply!(wtmp, a)
-    Yeppp.add!(w, w, wtmp)
-     
+    w .= w .+ wtmp
+
     # for ind in 1:n
     #     @inbounds w[ind] = a * log(xdata[ind]) + b * log(1 - xdata[ind])
     # end
-    
+
     add!(w, -lbeta(a+1, b+1))
-    Yeppp.exp!(w, w)
+    w .= exp.(w)
     nothing
 end
 function betakernel(x::Real, logxdata::RealVector, log1_xdata::RealVector, h::Real, w::Vector, n::Int)
@@ -68,12 +68,12 @@ function betakernel(x::Real, logxdata::RealVector, log1_xdata::RealVector, h::Re
     elseif x>1-2*h
         b = rhoxb(1-x, h) - 1
     end
-    
+
     for ind in 1:n
         @inbounds w[ind] = a * logxdata[ind] + b * log1_xdata[ind]
     end
     add!(w, -lbeta(a+1, b+1))
-    Yeppp.exp!(w, w)
+    w .= exp.(w)
     nothing
 end
 #f̂(x) = 1/n ∑ᵢ K(xᵢ;x /b+1, b )
@@ -87,15 +87,15 @@ function gammakernel(x::Real, xdata::RealVector, h::Real, w::Vector, n::Int)
         rhob = 0.25 * rhob * rhob + 1.0
     end
 
-    Yeppp.log!(w, xdata)
+    w .= log.(xdata)
     multiply!(w, rhob-1.0)
     tmp = -rhob*log(h)-lgamma(rhob)
     add!(w, tmp)
     h1 = 1/h
     for ind in 1:n
         @inbounds w[ind] -= xdata[ind] * h1
     end
-    Yeppp.exp!(w, w)
+    w .= exp.(w)
     nothing
 end
 function gammakernel(x::Real, xdata::RealVector, logxdata::RealVector, h::Real, w::Vector, n::Int)
@@ -115,7 +115,7 @@ function gammakernel(x::Real, xdata::RealVector, logxdata::RealVector, h::Real,
     for ind in 1:n
         @inbounds w[ind] -= xdata[ind] * h1
     end
-    Yeppp.exp!(w, w)
+    w .= exp.(w)
     nothing
 end
 
@@ -134,8 +134,8 @@ function gaussiankernel(x::Real, xdata::RealVector, h::Real, w::Vector, n::Int)
         @inbounds w[ind]=-0.5*abs2((x - xdata[ind])*h1) - tmp
     end
     # add!(w, tmp, n)
-    Yeppp.exp!(w, w)
-    
+    w .= exp.(w)
+
     nothing
 end
 function ekernel(x::Real, xdata::RealVector, h::Real, w::Vector, n::Int)

test/runtests.jl

@@ -1,5 +1,6 @@
 using KernelEstimator
-using Base.Test
-
+using Test
+using Random
+using StatsBase
 # write your own tests here
 include("testreg.jl")

test/testreg.jl

@@ -1,9 +1,12 @@
 
 ##Univariate kerneldensity and regression
 using Distributions
-srand(2017);
+Random.seed!(2017);
+
+linreg(x, y) = hcat(fill!(similar(x), 1), x) \ y
+
 x=rand(Normal(10), 500)
-xeval=linspace(minimum(x), maximum(x), 100)
+xeval=range(minimum(x), stop=maximum(x), length=100)
 h = bwlscv(x, gaussiankernel)
 @test h>0
 denvalues=kerneldensity(x, xeval=xeval)
@@ -44,7 +47,7 @@ regfit = x*inv(x'*x)*x'*y
 
 ###Bounded gamma kernel density and regression
 x = rand(Gamma(4,2), 500)
-xeval = linspace(0.01,20, 100)
+xeval = range(0.01, stop=20, length=100)
 h = bwlscv(x, gammakernel)
 @test h>0
 denvalues = kerneldensity(x, xeval=xeval, kernel=gammakernel, lb=0.0)
@@ -61,7 +64,7 @@ yfit1=npr(x, y, xeval=x, reg=locallinear, kernel=gammakernel, lb=0.0)
 
 #bounded beta kernel density and regression
 x = rand(Beta(4,2), 500) * 10
-xeval = linspace(0, 10, 100)
+xeval = range(0, stop=10, length=100)
 h = bwlscv(x./10, betakernel)
 @test h>0
 denvalues=kerneldensity(x, xeval=xeval, kernel=betakernel,h=h, lb=0.0,ub=10.0)