Updates for Julia 1.0 (#132)

.travis.yml

@@ -3,8 +3,8 @@ language: julia
 os:
   - linux
 julia:
-  - 0.6
   - 0.7
+  - 1.0
   - nightly
 notifications:
   email: false
@@ -13,5 +13,5 @@ notifications:
 #  - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
 #  - julia -e 'Pkg.clone(pwd()); Pkg.build("HypothesisTests"); Pkg.test("HypothesisTests"; coverage=true)'
 after_success:
-  - julia -e 'cd(Pkg.dir("HypothesisTests")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())';
-  - julia -e 'Pkg.add("Documenter"); cd(Pkg.dir("HypothesisTests")); include(joinpath("docs", "make.jl"))'
+  - julia -e 'using Pkg; Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())';
+  - julia -e 'using Pkg; Pkg.add("Documenter"); include(joinpath("docs", "make.jl"))'

REQUIRE

@@ -1,7 +1,6 @@
-julia 0.6
-Distributions 0.10.0
+julia 0.7
+Distributions 0.16.3
 Roots
-StatsBase 0.9.0
-Compat 0.62.0
-Rmath 0.1.2
-Combinatorics
+StatsBase 0.25.0
+Rmath 0.5.0
+Combinatorics 0.7.0

docs/make.jl

@@ -16,7 +16,7 @@ makedocs(
 deploydocs(
     repo = "github.com/JuliaStats/HypothesisTests.jl.git",
     target = "build",
-    julia  = "0.6",
+    julia  = "1.0",
     deps = nothing,
     make = nothing
 )

src/HypothesisTests.jl

@@ -22,12 +22,10 @@
 # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
 # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
-__precompile__()
-
 module HypothesisTests
 
-using Compat.Random, Compat.LinearAlgebra
-using Distributions, Roots, StatsBase, Compat
+using Statistics, Random, LinearAlgebra
+using Distributions, Roots, StatsBase
 using Combinatorics: combinations, permutations
 using Rmath: pwilcox, psignrank
 
@@ -112,7 +110,7 @@ function Base.show(io::IO, test::T) where T<:HypothesisTest
     println(io, "    value under h_0:         $param_under_h0")
     println(io, "    point estimate:          $param_estimate")
     if has_ci
-        ci = map(x -> Compat.round.(x, digits=4, base=10), StatsBase.confint(test))
+        ci = map(x -> round.(x, digits=4, base=10), StatsBase.confint(test))
         println(io, "    95% confidence interval: $ci")
     end
     println(io)

src/anderson_darling.jl

@@ -264,7 +264,7 @@ function adkvals(Z⁺, N, samples)
             fij[i, searchsortedfirst(Z⁺, s)] += 1
         end
     end
-    ljs = Compat.sum(fij, dims=1)
+    ljs = sum(fij, dims=1)
 
     A²k = A²km = 0.
     for i in 1:k

src/durbin_watson.jl

@@ -22,10 +22,6 @@
 # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
 # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
-if VERSION < v"0.7.0-DEV.4591"
-    tr(A::AbstractMatrix) = trace(A)
-end
-
 export DurbinWatsonTest
 
 struct DurbinWatsonTest <: HypothesisTest
@@ -197,7 +193,7 @@ function pvalue(x::DurbinWatsonTest; tail=:both)
 
         if p_temp < 0.0 || p_temp > 1.0
             # println(p_temp)
-            Compat.@warn("Exact p-values outside [0,1]. Approximate p-values reported instead.")
+            @warn("Exact p-values outside [0,1]. Approximate p-values reported instead.")
             exact_problem_flag = 1
         end
     end

src/kolmogorov_smirnov.jl

@@ -65,7 +65,7 @@ Implements: [`pvalue`](@ref)
 """
 function ExactOneSampleKSTest(x::AbstractVector{T}, d::UnivariateDistribution) where T<:Real
     if length(x) > length(unique(x))
-        Compat.@warn("This test is inaccurate with ties")
+        @warn("This test is inaccurate with ties")
     end
 
     ExactOneSampleKSTest(ksstats(x, d)...)
@@ -109,7 +109,7 @@ Implements: [`pvalue`](@ref)
 """
 function ApproximateOneSampleKSTest(x::AbstractVector{T}, d::UnivariateDistribution) where T<:Real
     if length(x) > length(unique(x))
-        Compat.@warn("This test is inaccurate with ties")
+        @warn("This test is inaccurate with ties")
     end
 
     ApproximateOneSampleKSTest(ksstats(x, d)...)
@@ -164,7 +164,7 @@ Implements: [`pvalue`](@ref)
 function ApproximateTwoSampleKSTest(x::AbstractVector{T}, y::AbstractVector{S}) where {T<:Real, S<:Real}
     n_x, n_y = length(x), length(y)
     if n_x+n_y > length(unique([x; y]))
-        Compat.@warn("This test is inaccurate with ties")
+        @warn("This test is inaccurate with ties")
     end
 
     ApproximateTwoSampleKSTest(ksstats(x, y)...)

src/kruskal_wallis.jl

@@ -70,7 +70,7 @@ Implements: [`pvalue`](@ref)
 function KruskalWallisTest(groups::AbstractVector{T}...) where T<:Real
     (H, R_i, tieadj, n_i) = kwstats(groups...)
     if length(groups)<=3 && any(n_i .< 6)
-        Compat.@warn("This test is only asymptotically correct and might be inaccurate for the given group size")
+        @warn("This test is only asymptotically correct and might be inaccurate for the given group size")
     end
     df = length(groups) - 1
     KruskalWallisTest(n_i, df, R_i, H, tieadj)

src/power_divergence.jl

@@ -97,7 +97,7 @@ end
 
 # Bootstrap
 function ci_bootstrap(x::PowerDivergenceTest,alpha::Float64, iters::Int64)
-    m = Compat.mapslices(x -> quantile(x, [alpha / 2, 1 - alpha / 2]), rand(Multinomial(x.n, convert(Vector{Float64}, x.thetahat)),iters) / x.n, dims=2)
+    m = mapslices(x -> quantile(x, [alpha / 2, 1 - alpha / 2]), rand(Multinomial(x.n, convert(Vector{Float64}, x.thetahat)),iters) / x.n, dims=2)
     Tuple{Float64,Float64}[(boundproportion(m[i,1]), boundproportion(m[i,2])) for i in 1:length(x.thetahat)]
 end
 
@@ -285,8 +285,8 @@ function PowerDivergenceTest(x::AbstractMatrix{T}; lambda::U=1.0, theta0::Vector
     (!isfinite(nrows) || !isfinite(ncols) || !isfinite(nrows*ncols)) && throw(ArgumentError("invalid number of rows or columns"))
 
     if nrows > 1 && ncols > 1
-        rowsums = Compat.sum(x, dims=2)
-        colsums = Compat.sum(x, dims=1)
+        rowsums = sum(x, dims=2)
+        colsums = sum(x, dims=1)
         df = (nrows - 1) * (ncols - 1)
         thetahat = x ./ n
         xhat = rowsums * colsums / n

test/anderson_darling.jl

@@ -1,87 +1,86 @@
-using HypothesisTests, Distributions, Compat.Test
+using HypothesisTests, Distributions, Test, Random
 using HypothesisTests: default_tail
-using Compat.Random
 
 @testset "Anderson-Darling" begin
 @testset "One sample test" begin
-	n = 1000
-	srand(1984948)
-
-	x = rand(Normal(), n)
-	t = OneSampleADTest(x, Normal())
-	@test t.A² ≈ 0.2013 atol=0.1^4
-	@test pvalue(t) ≈ 0.99 atol=0.1^4
-	@test default_tail(t) == :right
-
-	x = rand(DoubleExponential(), n)
-	t = OneSampleADTest(x, Normal())
-	@test t.A² ≈ 10.7439 atol=0.1^4
-	@test pvalue(t) ≈ 0.0 atol=0.1^4
-
-	x = rand(Cauchy(), n)
-	t = OneSampleADTest(x, Normal())
-	@test pvalue(t) ≈ 0.0 atol=0.1^4
-
-	x = rand(LogNormal(), n)
-	t = OneSampleADTest(x, Normal())
-	@test pvalue(t) ≈ 0.0 atol=0.1^4
-
-	x = rand(Uniform(), n)
-	t = OneSampleADTest(x, Uniform())
-	@test pvalue(t) ≈ 0.145 atol=0.1^4
-
-	x = rand(Uniform(0, 1.8), n)
-	t = OneSampleADTest(x, Uniform())
-	@test pvalue(t) ≈ 0.5112 atol=0.1^4
-
-	x = rand(Exponential(), n)
-	t = OneSampleADTest(x, Exponential())
-	@test pvalue(t) ≈ 0.9427 atol=0.1^4
+    n = 1000
+    Random.seed!(1984948)
+
+    x = rand(Normal(), n)
+    t = OneSampleADTest(x, Normal())
+    @test t.A² ≈ 0.2013 atol=0.1^4
+    @test pvalue(t) ≈ 0.99 atol=0.1^4
+    @test default_tail(t) == :right
+
+    x = rand(DoubleExponential(), n)
+    t = OneSampleADTest(x, Normal())
+    @test t.A² ≈ 10.7439 atol=0.1^4
+    @test pvalue(t) ≈ 0.0 atol=0.1^4
+
+    x = rand(Cauchy(), n)
+    t = OneSampleADTest(x, Normal())
+    @test pvalue(t) ≈ 0.0 atol=0.1^4
+
+    x = rand(LogNormal(), n)
+    t = OneSampleADTest(x, Normal())
+    @test pvalue(t) ≈ 0.0 atol=0.1^4
+
+    x = rand(Uniform(), n)
+    t = OneSampleADTest(x, Uniform())
+    @test pvalue(t) ≈ 0.145 atol=0.1^4
+
+    x = rand(Uniform(0, 1.8), n)
+    t = OneSampleADTest(x, Uniform())
+    @test pvalue(t) ≈ 0.5112 atol=0.1^4
+
+    x = rand(Exponential(), n)
+    t = OneSampleADTest(x, Exponential())
+    @test pvalue(t) ≈ 0.9427 atol=0.1^4
 end
 
 @testset "k-sample test" begin
-	srand(948574875)
-	samples = Any[
-	    [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0],
-	    [39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8],
-	    [34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0],
-	    [34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8]
-	]
-
-	t = KSampleADTest(samples...)
-	@test t.A²k ≈ 8.3926 atol=0.1^4
-	@test t.σ ≈ 1.2038 atol=0.1^4
-	@test pvalue(t) ≈ 0.0022 atol=0.1^4
-	@test default_tail(t) == :right
-
-	ts = KSampleADTest(samples..., nsim = 20000);
-	@test pvalue(ts) ≈ 0.00155 atol=0.1^3
-
-	t = KSampleADTest(samples..., modified = false)
-	@test t.A²k ≈ 8.3559 atol=0.1^4
-	@test t.σ ≈ 1.2038 atol=0.1^4
-	@test pvalue(t) ≈ 0.0023 atol=0.1^4
-
-	ts = KSampleADTest(samples..., modified = false, nsim = 20000);
-	@test pvalue(ts) ≈ 0.00150 atol=0.1^3
+    Random.seed!(948574875)
+    samples = Any[
+        [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0],
+        [39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8],
+        [34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0],
+        [34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8]
+    ]
+
+    t = KSampleADTest(samples...)
+    @test t.A²k ≈ 8.3926 atol=0.1^4
+    @test t.σ ≈ 1.2038 atol=0.1^4
+    @test pvalue(t) ≈ 0.0022 atol=0.1^4
+    @test default_tail(t) == :right
+
+    ts = KSampleADTest(samples..., nsim = 20000);
+    @test pvalue(ts) ≈ 0.00155 atol=0.1^3
+
+    t = KSampleADTest(samples..., modified = false)
+    @test t.A²k ≈ 8.3559 atol=0.1^4
+    @test t.σ ≈ 1.2038 atol=0.1^4
+    @test pvalue(t) ≈ 0.0023 atol=0.1^4
+
+    ts = KSampleADTest(samples..., modified = false, nsim = 20000);
+    @test pvalue(ts) ≈ 0.00150 atol=0.1^3
 end
 
 @testset "more tests" begin
-	srand(31412455)
-	samples = Any[rand(Normal(), 50), rand(Normal(0.5), 30)]
-	t = KSampleADTest(samples...)
-	@test pvalue(t) < 0.05
+    Random.seed!(31412455)
+    samples = Any[rand(Normal(), 50), rand(Normal(0.5), 30)]
+    t = KSampleADTest(samples...)
+    @test pvalue(t) < 0.05
 
-	samples = Any[rand(Normal(), 50), rand(Normal(), 30), rand(Normal(), 20)]
-	t = KSampleADTest(samples...)
-	@test pvalue(t) > 0.05
+    samples = Any[rand(Normal(), 50), rand(Normal(), 30), rand(Normal(), 20)]
+    t = KSampleADTest(samples...)
+    @test pvalue(t) > 0.05
 
-	@test pvalue(OneSampleADTest(vcat(rand(Normal(),500), rand(Beta(2,2),500)), Beta(2,2))) ≈ 0.0 atol=0.1^4
+    @test pvalue(OneSampleADTest(vcat(rand(Normal(),500), rand(Beta(2,2),500)), Beta(2,2))) ≈ 0.0 atol=0.1^4
 
-	n = 1000
-	x = rand(Exponential(), n)
-	@test pvalue(KSampleADTest(rand(1000),randn(1000))) ≈ 0.0 atol=eps()
-	@test pvalue(KSampleADTest(x,x,x,x,x,x)) ≈ 1.0
+    n = 1000
+    x = rand(Exponential(), n)
+    @test pvalue(KSampleADTest(rand(1000),randn(1000))) ≈ 0.0 atol=eps()
+    @test pvalue(KSampleADTest(x,x,x,x,x,x)) ≈ 1.0
 end
 
 end

test/augmented_dickey_fuller.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.LinearAlgebra, Compat.Test
+using HypothesisTests, LinearAlgebra, Test
 
 @testset "Augmented Dickey-Fuller" begin
 sim_data_h0 = [

test/binomial.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Binomial" begin

test/box_test.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Box-Pierce and Ljung-Box" begin

test/breusch_godfrey.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Breusch-Godfrey" begin

test/circular.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Circular" begin

test/common.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 
 mutable struct TestTest <: HypothesisTests.HypothesisTest end
 

test/durbin_watson.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Durbin-Watson" begin

test/fisher.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Fisher" begin

test/jarque_bera.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Jarque-Bera" begin

test/kolmogorov_smirnov.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Distributions, Compat.Test
+using HypothesisTests, Distributions, Test
 
 @testset "Kolmogorov-Smirnov" begin
 # sample drawn from uniform distribution

test/kruskal_wallis.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Kruskal-Wallis" begin

test/mann_whitney.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using HypothesisTests: default_tail
 
 @testset "Mann-Whitney" begin

test/permutation.jl

@@ -1,12 +1,12 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 
 @testset "Permutation" begin
 @test isapprox(pvalue(ExactPermutationTest([1,2,3],[4,5,6],mean), tail=:both), 1.0)
 @test isapprox(pvalue(ExactPermutationTest([4,5,6],[1,2,3],mean), tail=:both), 1.0)
 @test isapprox(pvalue(ExactPermutationTest([1,2,3],[4,5,6],mean), tail=:left), 0.05)
 @test isapprox(pvalue(ExactPermutationTest([4,5,6],[1,2,3],mean), tail=:right), 0.05)
 
-srand(12345)
+Random.seed!(12345)
 @test isapprox(pvalue(ApproximatePermutationTest([1,2,3],[4,5,6],mean,100), tail=:both), 1.0)
 @test isapprox(pvalue(ApproximatePermutationTest([4,5,6],[1,2,3],mean,100), tail=:both), 1.0)
 @test isapprox(pvalue(ApproximatePermutationTest([1,2,3],[4,5,6],mean,100), tail=:left), 0.08)

test/power_divergence.jl

@@ -1,4 +1,4 @@
-using HypothesisTests, Compat.Test
+using HypothesisTests, Test
 using StatsBase
 using HypothesisTests: default_tail