Merge pull request #101 from juliangehring/develop

Release v0.4.0

Author: juliangehring

.appveyor.yml

@@ -1,12 +1,9 @@
 environment:
   matrix:
-  - 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/x64/julia-latest-win64.exe"
+  - JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x64/0.7/julia-0.7-latest-win64.exe"
 
 matrix:
   fast_finish: true
-  allow_failures:
-    - JULIA_URL: "https://julialangnightlies-s3.julialang.org/bin/winnt/x64/julia-latest-win64.exe"
 
 notifications:
   - provider: Email

.travis.yml

@@ -4,13 +4,10 @@ os:
   - linux
 
 julia:
-  - 0.6
-  - nightly
+  - 0.7
 
 matrix:
   fast_finish: true
-  allow_failures:
-    - julia: nightly
 
 notifications:
   email:
@@ -23,5 +20,4 @@ script:
 
 after_success:
   - julia -e 'cd(Pkg.dir("MultipleTesting")); Pkg.add("Coverage"); using Coverage; Codecov.submit(Codecov.process_folder(), flags="julia_$(VERSION.major)_$(VERSION.minor)")'
-  - julia -e 'Pkg.add("Documenter")'
-  - julia -e 'cd(Pkg.dir("MultipleTesting")); include(joinpath("docs", "make.jl"))'
+  - julia -e 'cd(Pkg.dir("MultipleTesting")); Pkg.add("Documenter"); include(joinpath("docs", "make.jl"))'

NEWS.md

@@ -1,5 +1,20 @@
 # MultipleTesting.jl News and Changes
 
+## Version 0.4.0
+
+### Changes
+
+#### User-facing changes
+
+- Require julia v0.7 as the minimum version, drop support for julia v0.6 
+- Describe package features in the documentation
+
+
+### Support
+
+- Requires julia 0.7
+
+
 ## Version 0.3.1
 
 ### New Features

REQUIRE

@@ -1,4 +1,4 @@
-julia 0.6
-StatsBase 0.15.0
-Distributions 0.14.1
+julia 0.7
+StatsBase 0.24.0
+Distributions 0.16.1
 SpecialFunctions 0.3.8

docs/make.jl

@@ -24,7 +24,7 @@ makedocs(
 
 deploydocs(
     repo = "github.com/juliangehring/MultipleTesting.jl.git",
-    julia = "0.6",
+    julia = "0.7",
     target = "build",
     deps   = nothing,
     make   = nothing

docs/src/index.md

@@ -8,7 +8,110 @@ end
 ```
 
 
-## Package Features
+The `MultipleTesting` package offers common algorithms for p-value adjustment
+and combination as well as the estimation of the proportion π₀ of true null
+hypotheses.
+
+![xkcd p-value guide](https://imgs.xkcd.com/comics/p_values.png)
+
+
+## Features
+
+### Adjustment of p-values
+
+* Bonferroni
+* Benjamini-Hochberg
+* Adaptive Benjamini-Hochberg with known π₀ or π₀ estimation method (see section below)
+* Benjamini-Yekutieli
+* Benjamini-Liu
+* Hochberg
+* Holm
+* Hommel
+* Sidak
+* Forward Stop
+* Barber-Candès
+
+```julia
+adjust(pvals, Bonferroni())
+adjust(pvals, BenjaminiHochberg())
+adjust(pvals, BenjaminiHochbergAdaptive(0.9))
+adjust(pvals, BenjaminiHochbergAdaptive(Storey()))
+adjust(pvals, BenjaminiYekutieli())
+adjust(pvals, BenjaminiLiu())
+adjust(pvals, Hochberg())
+adjust(pvals, Holm())
+adjust(pvals, Hommel())
+adjust(pvals, Sidak())
+adjust(pvals, ForwardStop())
+adjust(pvals, BarberCandes())
+```
+
+The adjustment can also be performed on the `k` smallest out of `n` p-values:
+
+```julia
+adjust(pvals, n, PValueAdjustmentMethod)
+```
+
+
+### Estimation of π₀
+
+* Storey
+* Storey's closed-form bootstrap
+* Least Slope
+* Two Step
+* RightBoundary (Storey's estimate with dynamically chosen λ)
+* Beta-Uniform Mixture (BUM)
+* Censored BUM
+* Flat Grenander
+* Oracle for known π₀
+
+```julia
+estimate_pi0(pvals, Storey())
+estimate_pi0(pvals, StoreyBootstrap())
+estimate_pi0(pvals, LeastSlope())
+estimate_pi0(pvals, TwoStep())
+estimate_pi0(pvals, TwoStep(0.05))
+estimate_pi0(pvals, TwoStep(0.05, BenjaminiHochbergAdaptive(0.9))
+estimate_pi0(pvals, RightBoundary())
+estimate_pi0(pvals, CensoredBUM())
+estimate_pi0(pvals, BUM())
+estimate_pi0(pvals, FlatGrenander())
+estimate_pi0(pvals, Oracle(0.9))
+```
+
+
+### Combination of p-values
+
+* Fisher
+* Stouffer, optionally with weights
+* Logit
+* Tippett
+* Simes
+* Wilkinson
+* Minimum of adjusted p-values
+
+```julia
+combine(pvals, FisherCombination())
+combine(pvals, StoufferCombination())
+combine(pvals, weights, StoufferCombination())
+combine(pvals, LogitCombination())
+combine(pvals, TippettCombination())
+combine(pvals, SimesCombination())
+combine(pvals, WilkinsonCombination(rank))
+combine(pvals, MinimumCombination(PValueAdjustment()))
+```
+
+
+### Higher criticism
+
+* Higher criticism scores
+* Higher criticism threshold
+
+```julia
+estimate(pvals, HigherCriticismScores())
+estimate(pvals, HigherCriticismThreshold())
+```
+
 
 ## Manual
 

src/MultipleTesting.jl

@@ -17,6 +17,8 @@ import Distributions: estimate
 
 import SpecialFunctions: digamma
 
+import Random: shuffle!
+
 export
     PValues,
     adjust,

src/combinations.jl

@@ -166,7 +166,7 @@ function combine(pValues::PValues{T}, method::StoufferCombination)::T where T<:A
     if minimum(pValues) == 0 || maximum(pValues) == 1
         return NaN
     end
-    z = cquantile.(Normal(), pValues)
+    z = cquantile.(Ref(Normal()), pValues)
     z = sum(z) ./ sqrt(n)
     p = ccdf(Normal(), z)
     return p
@@ -180,7 +180,7 @@ function combine(pValues::PValues{T}, weights::AbstractVector{R}, method::Stouff
     if minimum(pValues) == 0 || maximum(pValues) == 1
         return NaN
     end
-    z = cquantile.(Normal(), pValues) .* weights
+    z = cquantile.(Ref(Normal()), pValues) .* weights
     z = sum(z) ./ sqrt(sum(abs2, weights))
     p = ccdf(Normal(), z)
     return p
@@ -307,7 +307,7 @@ function combine(pValues::PValues{T}, method::WilkinsonCombination)::T where T<:
     if rank < 1 || rank > n
         throw(ArgumentError("Rank must be in 1,..,$(n)"))
     end
-    p_rank = select(pValues, rank) # faster than `sort(pValues)[rank]`
+    p_rank = partialsort(pValues, rank)
     p = cdf(Beta(rank, n-rank+1), p_rank)
     return p
 end

src/higher-criticism.jl

@@ -36,7 +36,7 @@ function estimate(pValues::PValues{T}, method::HigherCriticismScores) where T<:A
     denom = F .* (one(T) .- F) ./ n
     # avoid denominator of 0 for last value
     idx0 = denom .== 0
-    denom[idx0] = minimum(denom[.!idx0]) .+ eps()  # conservative
+    denom[idx0] .= minimum(denom[.!idx0]) .+ eps()  # conservative
     hcs = abs.(F .- pValues) ./ sqrt.(denom)
     return hcs
 end
@@ -68,6 +68,6 @@ struct HigherCriticismThreshold
 end
 
 function estimate(pValues::PValues{T}, method::HigherCriticismThreshold) where T<:AbstractFloat
-    idx_hcv = indmax(estimate(pValues, HigherCriticismScores()))
+    idx_hcv = argmax(estimate(pValues, HigherCriticismScores()))
     return pValues[idx_hcv]
 end

src/model.jl

@@ -43,8 +43,7 @@ partitioning the empirical distribution of p-values. Bioinformatics 19,
 
 """
 function BetaUniformMixtureModel(π0::AbstractFloat, α::AbstractFloat = 0.5, β::AbstractFloat = 1.0)
-    if !isin(π0, 0., 1.)
-        throw(DomainError())
-    end
-    MixtureModel([Beta(α, β), Uniform()], [1-π0, π0])
+    isin(π0, 0, 1) || throw(DomainError("π0 must be in [0, 1]"))
+    bum = MixtureModel([Beta(α, β), Uniform()], [1-π0, π0])
+    return bum
 end