Merge pull request #78 from juliangehring/develop

Release v2.3.0

.codecov.yml

@@ -0,0 +1,5 @@
+coverage:
+  precision: 1
+  round: nearest
+
+comment: false

.github/workflows/ci.yml

@@ -0,0 +1,32 @@
+name: Testing
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  test:
+    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        version:
+          - '1.0'
+          - '1'
+        os:
+          - ubuntu-latest
+          - macOS-latest
+          - windows-latest
+        arch:
+          - x64
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@v1
+        with:
+          version: ${{ matrix.version }}
+          arch: ${{ matrix.arch }}
+      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-runtest@latest
+      - uses: julia-actions/julia-uploadcodecov@latest
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/documentation.yml

@@ -0,0 +1,20 @@
+name: Documentation
+
+on:
+  push:
+    branches:
+      - master
+    tags: [v*]
+  pull_request:
+
+jobs:
+  Documenter:
+    name: Documentation
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-docdeploy@latest
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}

NEWS.md

@@ -1,5 +1,16 @@
 # Bootstrap.jl News and Changes
 
+## Version 2.3.0
+
+### Changes
+
+- Performance improvements through parameterized types. Contributed by David Widmann (@devmotion) (#66).
+- Specialized Jack-Knife estimator. Contributed by David Widmann (@devmotion) (#67).
+- Switch to Github Actions for testing and documentation generation (#75).
+- Update links to external webpages in the documentation (#75).
+- Extend support to `Distributions` v0.24 (#76).
+
+
 ## Version 2.2.1
 
 ### Changes

Project.toml

@@ -3,7 +3,7 @@ uuid = "e28b5b4c-05e8-5b66-bc03-6f0c0a0a06e0"
 license = "MIT"
 authors = "Julian Gehring"
 repository = "https://github.com/juliangehring/Bootstrap.jl"
-version = "2.2.1"
+version = "2.3.0"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
@@ -15,7 +15,7 @@ StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 
 [compat]
 DataFrames = "0.19, 0.20, 0.21, 0.22"
-Distributions = "0.23"
+Distributions = "0.23, 0.24"
 GLM = "1.3"
 StatsBase = "0.33"
 StatsModels = "0.6"

README.md

@@ -27,14 +27,6 @@ Bootstrapping is a widely applicable technique for statistical estimation.
   - Bias-corrected and accelerated (BCa) (`BCaConfInt`)
 
 
-## Package Status
-
-Reports on package builds for all platforms and test coverage are collected on
-the [package status page](status.md).
-
-The package uses [semantic versioning](https://semver.org/).
-
-
 ## Installation
 
 The `Bootstrap` package is part of the Julia ecosphere and the latest release
@@ -136,3 +128,16 @@ requests or issues if you have suggestions for changes, ideas or questions.
   the term _bootstrapping_ is also used in different other contexts. You can check
   wikipedia for a longer list of
   [meanings associated with bootstrapping](https://en.wikipedia.org/wiki/Bootstrapping_(disambiguation)).
+
+
+## Package Status
+
+[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliangehring.github.io/Bootstrap.jl/stable)
+
+[![DOI](https://zenodo.org/badge/25918430.svg)](https://zenodo.org/badge/latestdoi/25918430)
+
+![Testing](https://github.com/juliangehring/Bootstrap.jl/workflows/Testing/badge.svg)
+
+[![Coverage](https://codecov.io/gh/juliangehring/Bootstrap.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/juliangehring/Bootstrap.jl)
+
+The package uses [semantic versioning](https://semver.org/).

docs/src/references.md

@@ -7,12 +7,12 @@
 
 * Davison, A. C.: [Bootstrap Methods and Their Application](http://statwww.epfl.ch/davison/BMA/). Cambridge University Press, 1997
 
-* Efron, B.: [Bootstrap Methods: Another Look at the Jackknife](https://projecteuclid.org/euclid.aos/1176344552). The Annals of Statistics 7, no. 1 (January 1979): 1–26. [doi:10.1214/aos/1176344552](http://dx.doi.org/10.1214/aos/1176344552)
+* Efron, B.: [Bootstrap Methods: Another Look at the Jackknife](https://projecteuclid.org/euclid.aos/1176344552). The Annals of Statistics 7, no. 1 (January 1979): 1–26. [doi:10.1214/aos/1176344552](https://dx.doi.org/10.1214/aos/1176344552)
 
 
 # Algorithms
 
-* Gleason, John R. [Algorithms for Balanced Bootstrap Simulations](http://www.jstor.org/stable/2685134). The American Statistician 42, no. 4 (November 1, 1988): 263–66. [doi:10.2307/2685134](http://dx.doi.org/10.2307/2685134)
+* Gleason, John R. [Algorithms for Balanced Bootstrap Simulations](https://www.jstor.org/stable/2685134). The American Statistician 42, no. 4 (November 1, 1988): 263–66. [doi:10.2307/2685134](https://dx.doi.org/10.2307/2685134)
 
 
 # Software

src/bootsampling.jl

@@ -1,21 +1,22 @@
 abstract type Model end
 
-struct SimpleModel{T} <: Model
+struct SimpleModel{T,A,K} <: Model
     class::T
-    args::Tuple
-    kwargs::Tuple
+    args::A
+    kwargs::K
 end
 
-Model(class, args...; kwargs...) = SimpleModel(class, tuple(args...), tuple(kwargs...))
+Model(class, args...; kwargs...) = SimpleModel(class, args, kwargs)
 
-struct FormulaModel{T} <: Model
+struct FormulaModel{T,A,K} <: Model
     class::T
     formula::FormulaTerm
-    args::Tuple
-    kwargs::Tuple
+    args::A
+    kwargs::K
 end
 
-Model(class, formula::FormulaTerm, args...; kwargs...) = FormulaModel(class, formula, tuple(args...), tuple(kwargs...))
+Model(class, formula::FormulaTerm, args...; kwargs...) =
+    FormulaModel(class, formula, args, kwargs)
 
 abstract type BootstrapSampling end
 
@@ -83,9 +84,9 @@ WildSampling(1000, mammen)
 ```
 
 """
-struct WildSampling <: BootstrapSampling
+struct WildSampling{F} <: BootstrapSampling
     nrun::Int
-    noise::Function
+    noise::F
 end
 
 
@@ -114,30 +115,30 @@ maximumEntropySampling(100, MaximumEntropyCache())
 NOTE: Implementation based off [pymeboot](https://github.com/kirajcg/pymeboot) as the original
 [R package](https://cran.r-project.org/web/packages/meboot/index.html) is GPL licensed.
 """
-struct MaximumEntropySampling <: BootstrapSampling
+struct MaximumEntropySampling{T} <: BootstrapSampling
     nrun::Int
-    cache::MaximumEntropyCache
+    cache::MaximumEntropyCache{T}
 end
 
 MaximumEntropySampling(nrun) = MaximumEntropySampling(nrun, MaximumEntropyCache())
 
 abstract type BootstrapSample end
 
-struct NonParametricBootstrapSample{T} <: BootstrapSample
-    t0::Tuple
-    t1::Tuple
-    statistic::Function
+struct NonParametricBootstrapSample{T,T0,T1,S,B} <: BootstrapSample
+    t0::T0
+    t1::T1
+    statistic::S
     data::T
-    sampling::BootstrapSampling
+    sampling::B
 end
 
-struct ParametricBootstrapSample{T,M} <: BootstrapSample
-    t0::Tuple
-    t1::Tuple
-    statistic::Function
+struct ParametricBootstrapSample{T,M,T0,T1,S,B} <: BootstrapSample
+    t0::T0
+    t1::T1
+    statistic::S
     data::T
     model::M
-    sampling::BootstrapSampling
+    sampling::B
 end
 
 
@@ -179,7 +180,10 @@ nvar(bs::BootstrapSample) = nvar(original(bs))
 
 tx(x) = tuple(x...)
 
-zeros_tuple(t, n) = tuple([zeros(typeof(x), n) for x in t]...)
+zeros_tuple(t::Tuple, m::Int) = _zeros_tuple(typeof(t), m)
+_zeros_tuple(::Type{Tuple{}}, m::Int) = ()
+_zeros_tuple(::Type{T}, m::Int) where T <: Tuple =
+    (zeros(Base.tuple_type_head(T), m), _zeros_tuple(Base.tuple_type_tail(T), m)...)
 
 """
 bootstrap(statistic, data, BasicSampling())
@@ -249,7 +253,7 @@ end
 
 struct ExactIterator{T}
     a::T
-    k::Int64
+    k::Int
 end
 
 exact(n::Int) = ExactIterator(1:n, n)
@@ -343,11 +347,11 @@ struct ResidualTerm{S,T,R} <: StatsModels.AbstractTerm
 end
 
 
-StatsModels.modelcols(t::ResidualTerm{S}, data) where {S} =
-    throw(ArgumentError("ResidualTerm: don't know how to generate model columns for $S"))
+StatsModels.modelcols(t::ResidualTerm, data) =
+    throw(ArgumentError("ResidualTerm: don't know how to generate model columns for $(typeof(t.sampling))"))
 StatsModels.modelcols(t::ResidualTerm{ResidualSampling}, data) =
     modelcols(t.t, data) + sample!(t.r0, t.r1)
-StatsModels.modelcols(t::ResidualTerm{WildSampling}, data) =
+StatsModels.modelcols(t::ResidualTerm{<:WildSampling}, data) =
     modelcols(t.t, data) + t.sampling.noise(t.r0)
 
 # we only ever create one of these terms after the model has been fit once so we don't
@@ -360,7 +364,7 @@ bootstrap(statistic, data, model, formula, sampling)
 bootstrap(statistic, data, model, formula, Wildsampling(nrun, noise))
 """
 function bootstrap(statistic::Function, data::AbstractDataFrame, model::FormulaModel,
-                   sampling::S) where {S <: BootstrapSampling}
+                   sampling::BootstrapSampling)
     class = model.class
     formula = apply_schema(model.formula, schema(model.formula, data), model.class)
 

src/confint.jl

@@ -10,8 +10,8 @@ BasicConfInt(0.95)
 ```
 
 """
-struct BasicConfInt <: ConfIntMethod
-    level::AbstractFloat
+struct BasicConfInt{T} <: ConfIntMethod
+    level::T
 end
 
 BasicConfInt() = BasicConfInt(_level)
@@ -25,14 +25,14 @@ PercentileConfInt(0.95)
 ```
 
 """
-struct PercentileConfInt <: ConfIntMethod
-    level::AbstractFloat
+struct PercentileConfInt{T} <: ConfIntMethod
+    level::T
 end
 
 PercentileConfInt() = PercentileConfInt(_level)
 
-struct NormalConfInt <: ConfIntMethod
-    level::AbstractFloat
+struct NormalConfInt{T} <: ConfIntMethod
+    level::T
 end
 
 
@@ -46,8 +46,8 @@ NormalConfInt(0.95)
 """
 NormalConfInt() = NormalConfInt(_level)
 
-struct BCaConfInt <: ConfIntMethod
-    level::AbstractFloat
+struct BCaConfInt{T} <: ConfIntMethod
+    level::T
 end
 
 
@@ -61,8 +61,8 @@ BCaConfInt(0.95)
 """
 BCaConfInt() = BCaConfInt(_level)
 
-struct StudentConfInt <: ConfIntMethod
-    level::AbstractFloat
+struct StudentConfInt{T} <: ConfIntMethod
+    level::T
 end
 
 """