Updates for Julia 0.6

.travis.yml

@@ -3,7 +3,7 @@ language: julia
 os:
   - linux
 julia:
-  - 0.5
+  - 0.6
   - nightly
 notifications:
   email: false

README.md

@@ -1,6 +1,5 @@
 # Jackknife.jl
 
-[![0.5](http://pkg.julialang.org/badges/Jackknife_0.5.svg)](http://pkg.julialang.org/?pkg=Jackknife)
 [![0.6](http://pkg.julialang.org/badges/Jackknife_0.6.svg)](http://pkg.julialang.org/?pkg=Jackknife)
 [![Travis](https://travis-ci.org/ararslan/Jackknife.jl.svg?branch=master)](https://travis-ci.org/ararslan/Jackknife.jl)
 [![Coveralls](https://coveralls.io/repos/github/ararslan/Jackknife.jl/badge.svg?branch=master)](https://coveralls.io/github/ararslan/Jackknife.jl?branch=master)

REQUIRE

@@ -1 +1 @@
-julia 0.5
+julia 0.6

appveyor.yml

@@ -1,7 +1,7 @@
 environment:
   matrix:
-  - JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x86/0.4/julia-0.4-latest-win32.exe"
-  - JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x64/0.4/julia-0.4-latest-win64.exe"
+  - JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x86/0.6/julia-0.6-latest-win32.exe"
+  - JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x64/0.6/julia-0.6-latest-win64.exe"
   - JULIA_URL: "https://julialangnightlies-s3.julialang.org/bin/winnt/x86/julia-latest-win32.exe"
   - JULIA_URL: "https://julialangnightlies-s3.julialang.org/bin/winnt/x64/julia-latest-win64.exe"
 

src/Jackknife.jl

@@ -9,7 +9,7 @@ Estimate the parameter `estimator(x)` for each subsample of `x`, systematically
 omitting each index one at a time. The result is a vector of length `length(x)-1`
 of parameter estimates.
 """
-function leaveoneout{T<:Real}(estimator::Function, x::AbstractVector{T})
+function leaveoneout(estimator::Function, x::AbstractVector{<:Real})
     length(x) > 1 || throw(ArgumentError("The sample must have size > 1"))
     inds = eachindex(x)
     return [estimator(x[filter(j -> j != i, inds)]) for i in inds]
@@ -22,7 +22,7 @@ end
 Compute the jackknife estimate of the variance of `estimator`, which is given as a
 function that computes a point estimate when passed a real-valued vector `x`.
 """
-function variance{T<:Real}(estimator::Function, x::AbstractVector{T})
+function variance(estimator::Function, x::AbstractVector{<:Real})
     θ = leaveoneout(estimator, x)
     n = length(x)
     return Base.var(θ) * (n - 1)^2 / n
@@ -35,7 +35,7 @@ end
 Compute the jackknife estimate of the bias of `estimator`, which is given as a
 function that computes a point estimate when passed a real-valued vector `x`.
 """
-function bias{T<:Real}(estimator::Function, x::AbstractVector{T})
+function bias(estimator::Function, x::AbstractVector{<:Real})
     θ = leaveoneout(estimator, x)
     return (length(x) - 1) * (mean(θ) - estimator(x))
 end
@@ -46,7 +46,7 @@ end
 
 Compute the bias-corrected jackknife estimate of the parameter `estimator(x)`.
 """
-function estimate{T<:Real}(estimator::Function, x::AbstractVector{T})
+function estimate(estimator::Function, x::AbstractVector{<:Real})
     θ = leaveoneout(estimator, x)
     n = length(x)
     return n * estimator(x) - (n - 1) * mean(θ)