move base/statistics.jl to Statistics stdlib

fix #27374

base/deprecated.jl

@@ -293,12 +293,6 @@ deprecate(Base, :DSP, 2)
 using .DSP
 export conv, conv2, deconv, filt, filt!, xcorr
 
-# PR #21709
-@deprecate cov(x::AbstractVector, corrected::Bool) cov(x, corrected=corrected)
-@deprecate cov(x::AbstractMatrix, vardim::Int, corrected::Bool) cov(x, dims=vardim, corrected=corrected)
-@deprecate cov(X::AbstractVector, Y::AbstractVector, corrected::Bool) cov(X, Y, corrected=corrected)
-@deprecate cov(X::AbstractVecOrMat, Y::AbstractVecOrMat, vardim::Int, corrected::Bool) cov(X, Y, dims=vardim, corrected=corrected)
-
 # PR #22325
 # TODO: when this replace is removed from deprecated.jl:
 # 1) rename the function replace_new from strings/util.jl to replace
@@ -1372,16 +1366,6 @@ export readandwrite
 @deprecate findmax(A::AbstractArray, dims)    findmax(A, dims=dims)
 @deprecate findmin(A::AbstractArray, dims)    findmin(A, dims=dims)
 
-@deprecate mean(A::AbstractArray, dims)                              mean(A, dims=dims)
-@deprecate varm(A::AbstractArray, m::AbstractArray, dims; kwargs...) varm(A, m; kwargs..., dims=dims)
-@deprecate var(A::AbstractArray, dims; kwargs...)                    var(A; kwargs..., dims=dims)
-@deprecate std(A::AbstractArray, dims; kwargs...)                    std(A; kwargs..., dims=dims)
-@deprecate cov(X::AbstractMatrix, dim::Int; kwargs...)               cov(X; kwargs..., dims=dim)
-@deprecate cov(x::AbstractVecOrMat, y::AbstractVecOrMat, dim::Int; kwargs...) cov(x, y; kwargs..., dims=dim)
-@deprecate cor(X::AbstractMatrix, dim::Int)                          cor(X, dims=dim)
-@deprecate cor(x::AbstractVecOrMat, y::AbstractVecOrMat, dim::Int)   cor(x, y, dims=dim)
-@deprecate median(A::AbstractArray, dims; kwargs...)                 median(A; kwargs..., dims=dims)
-
 @deprecate mapreducedim(f, op, A::AbstractArray, dims)     mapreduce(f, op, A, dims=dims)
 @deprecate mapreducedim(f, op, A::AbstractArray, dims, v0) mapreduce(f, op, v0, A, dims=dims)
 @deprecate reducedim(op, A::AbstractArray, dims)           reduce(op, A, dims=dims)
@@ -1703,6 +1687,9 @@ end
 # PR #25168
 @deprecate ipermute!(a, p::AbstractVector) invpermute!(a, p)
 
+# #27140, #27152
+@deprecate_moved linreg "StatsBase"
+
 # ?? more special functions to SpecialFunctions.jl
 @deprecate_moved gamma "SpecialFunctions"
 @deprecate_moved lgamma "SpecialFunctions"

base/exports.jl

@@ -626,21 +626,6 @@ export
     get_zero_subnormals,
     set_zero_subnormals,
 
-# statistics
-    cor,
-    cov,
-    mean!,
-    mean,
-    median!,
-    median,
-    middle,
-    quantile!,
-    quantile,
-    std,
-    stdm,
-    var,
-    varm,
-
 # iteration
     done,
     next,

base/range.jl

@@ -847,13 +847,6 @@ function sum(r::AbstractRange{<:Real})
                                      : (step(r) * l) * ((l-1)>>1))
 end
 
-function mean(r::AbstractRange{<:Real})
-    isempty(r) && throw(ArgumentError("mean of an empty range is undefined"))
-    (first(r) + last(r)) / 2
-end
-
-median(r::AbstractRange{<:Real}) = mean(r)
-
 function _in_range(x, r::AbstractRange)
     if step(r) == 0
         return !isempty(r) && first(r) == x

base/sysimg.jl

@@ -413,9 +413,6 @@ using .StackTraces
 
 include("initdefs.jl")
 
-# statistics
-include("statistics.jl")
-
 # worker threads
 include("threadcall.jl")
 
@@ -525,6 +522,7 @@ let
             :Pkg,
             :Test,
             :REPL,
+            :Statistics,
         ]
 
     maxlen = maximum(textwidth.(string.(stdlibs)))
@@ -746,7 +744,6 @@ end
     # @deprecate_stdlib kron        LinearAlgebra true
     @deprecate_stdlib ldltfact    LinearAlgebra true
     @deprecate_stdlib ldltfact!   LinearAlgebra true
-    @deprecate_stdlib linreg      LinearAlgebra true
     @deprecate_stdlib logabsdet   LinearAlgebra true
     @deprecate_stdlib logdet      LinearAlgebra true
     @deprecate_stdlib lu          LinearAlgebra true
@@ -896,6 +893,20 @@ end
     @deprecate_stdlib send           Sockets true
     @deprecate_stdlib TCPSocket      Sockets true
     @deprecate_stdlib UDPSocket      Sockets true
+
+    @deprecate_stdlib cor       Statistics true
+    @deprecate_stdlib cov       Statistics true
+    @deprecate_stdlib std       Statistics true
+    @deprecate_stdlib stdm      Statistics true
+    @deprecate_stdlib var       Statistics true
+    @deprecate_stdlib varm      Statistics true
+    @deprecate_stdlib mean!     Statistics true
+    @deprecate_stdlib mean      Statistics true
+    @deprecate_stdlib median!   Statistics true
+    @deprecate_stdlib median    Statistics true
+    @deprecate_stdlib middle    Statistics true
+    @deprecate_stdlib quantile! Statistics true
+    @deprecate_stdlib quantile  Statistics true
 end
 end
 

doc/src/base/math.md

@@ -177,21 +177,3 @@ Base.widemul
 Base.Math.@evalpoly
 Base.FastMath.@fastmath
 ```
-
-## Statistics
-
-```@docs
-Base.mean
-Base.mean!
-Base.std
-Base.stdm
-Base.var
-Base.varm
-Base.middle
-Base.median
-Base.median!
-Base.quantile
-Base.quantile!
-Base.cov
-Base.cor
-```

doc/src/manual/interfaces.md

@@ -65,7 +65,6 @@ julia> struct Squares
        end
 
 julia> Base.iterate(S::Squares, state=1) = state > S.count ? nothing : (state*state, state+1)
-
 ```
 
 With only [`iterate`](@ref) definition, the `Squares` type is already pretty powerful.
@@ -84,12 +83,16 @@ julia> for i in Squares(7)
 49
 ```
 
-We can use many of the builtin methods that work with iterables, like [`in`](@ref), [`mean`](@ref) and [`std`](@ref):
+We can use many of the builtin methods that work with iterables,
+like [`in`](@ref), or [`mean`](@ref) and [`std`](@ref) from the
+`Statistics` standard library module:
 
 ```jldoctest squaretype
 julia> 25 in Squares(10)
 true
 
+julia> using Statistics
+
 julia> mean(Squares(100))
 3383.5
 
@@ -388,8 +391,8 @@ julia> A[SquaresVector(3)]
  4.0
  9.0
 
-julia> mean(A)
-5.0
+julia> sum(A)
+45.0
 ```
 
 If you are defining an array type that allows non-traditional indexing (indices that start at

doc/src/manual/missing.md

@@ -294,7 +294,7 @@ julia> sum(skipmissing([1, missing]))
 
 This convenience function returns an iterator which filters out `missing` values
 efficiently. It can therefore be used with any function which supports iterators
-```jldoctest
+```jldoctest; setup = :(using Statistics)
 julia> maximum(skipmissing([3, missing, 2, 1]))
 3
 

stdlib/LinearAlgebra/docs/src/index.md

@@ -370,7 +370,6 @@ Base.inv(::AbstractMatrix)
 LinearAlgebra.pinv
 LinearAlgebra.nullspace
 Base.kron
-LinearAlgebra.linreg
 LinearAlgebra.exp(::StridedMatrix{<:LinearAlgebra.BlasFloat})
 LinearAlgebra.log(::StridedMatrix)
 LinearAlgebra.sqrt(::StridedMatrix{<:Real})

stdlib/LinearAlgebra/src/LinearAlgebra.jl

@@ -103,7 +103,6 @@ export
     ldiv!,
     ldlt!,
     ldlt,
-    linreg,
     logabsdet,
     logdet,
     lowrankdowndate,

stdlib/LinearAlgebra/src/generic.jl

@@ -1143,46 +1143,6 @@ isdiag(A::AbstractMatrix) = isbanded(A, 0, 0)
 isdiag(x::Number) = true
 
 
-"""
-    linreg(x, y)
-
-Perform simple linear regression using Ordinary Least Squares. Returns `a` and `b` such
-that `a + b*x` is the closest straight line to the given points `(x, y)`, i.e., such that
-the squared error between `y` and `a + b*x` is minimized.
-
-# Examples
-```julia
-using PyPlot
-x = 1.0:12.0
-y = [5.5, 6.3, 7.6, 8.8, 10.9, 11.79, 13.48, 15.02, 17.77, 20.81, 22.0, 22.99]
-a, b = linreg(x, y)          # Linear regression
-plot(x, y, "o")              # Plot (x, y) points
-plot(x, a + b*x)             # Plot line determined by linear regression
-```
-
-See also:
-
-`\\`, [`cov`](@ref), [`std`](@ref), [`mean`](@ref).
-
-"""
-function linreg(x::AbstractVector, y::AbstractVector)
-    # Least squares given
-    # Y = a + b*X
-    # where
-    # b = cov(X, Y)/var(X)
-    # a = mean(Y) - b*mean(X)
-    if size(x) != size(y)
-        throw(DimensionMismatch("x has size $(size(x)) and y has size $(size(y)), " *
-            "but these must be the same size"))
-    end
-    mx = mean(x)
-    my = mean(y)
-    # don't need to worry about the scaling (n vs n - 1) since they cancel in the ratio
-    b = Base.covm(x, mx, y, my)/Base.varm(x, mx)
-    a = my - b*mx
-    return (a, b)
-end
-
 # BLAS-like in-place y = x*α+y function (see also the version in blas.jl
 #                                          for BlasFloat Arrays)
 function axpy!(α, x::AbstractArray, y::AbstractArray)

stdlib/LinearAlgebra/test/generic.jl

@@ -89,53 +89,6 @@ n = 5 # should be odd
     end
 end
 
-@testset "linrange" begin
-    # make sure unequal input arrays throw an error
-    x = [2; 5; 6]
-    y = [3; 7; 10; 10]
-    @test_throws DimensionMismatch linreg(x, y)
-    x = [2 5 6]
-    y = [3; 7; 10]
-    @test_throws MethodError linreg(x, y)
-
-    # check (UnitRange, Array)
-    x = 1:12
-    y = [5.5; 6.3; 7.6; 8.8; 10.9; 11.79; 13.48; 15.02; 17.77; 20.81; 22.0; 22.99]
-    @test [linreg(x,y)...] ≈ [2.5559090909090867, 1.6960139860139862]
-    @test [linreg(view(x,1:6),view(y,1:6))...] ≈ [3.8366666666666642,1.3271428571428574]
-
-    # check (LinRange, UnitRange)
-    x = range(1.0, stop=12.0, length=100)
-    y = -100:-1
-    @test [linreg(x, y)...] ≈ [-109.0, 9.0]
-
-    # check (UnitRange, UnitRange)
-    x = 1:12
-    y = 12:-1:1
-    @test [linreg(x, y)...] ≈ [13.0, -1.0]
-
-    # check (LinRange, LinRange)
-    x = range(-5, stop=10, length=100)
-    y = range(50, stop=200, length=100)
-    @test [linreg(x, y)...] ≈ [100.0, 10.0]
-
-    # check (Array, Array)
-    # Anscombe's quartet (https://en.wikipedia.org/wiki/Anscombe%27s_quartet)
-    x123 = [10.0; 8.0; 13.0; 9.0; 11.0; 14.0; 6.0; 4.0; 12.0; 7.0; 5.0]
-    y1 = [8.04; 6.95; 7.58; 8.81; 8.33; 9.96; 7.24; 4.26; 10.84; 4.82; 5.68]
-    @test [linreg(x123,y1)...] ≈ [3.0,0.5] atol=15e-5
-
-    y2 = [9.14; 8.14; 8.74; 8.77; 9.26; 8.10; 6.12; 3.10; 9.13; 7.26; 4.74]
-    @test [linreg(x123,y2)...] ≈ [3.0,0.5] atol=10e-3
-
-    y3 = [7.46; 6.77; 12.74; 7.11; 7.81; 8.84; 6.08; 5.39; 8.15; 6.42; 5.73]
-    @test [linreg(x123,y3)...] ≈ [3.0,0.5] atol=10e-3
-
-    x4 = [8.0; 8.0; 8.0; 8.0; 8.0; 8.0; 8.0; 19.0; 8.0; 8.0; 8.0]
-    y4 = [6.58; 5.76; 7.71; 8.84; 8.47; 7.04; 5.25; 12.50; 5.56; 7.91; 6.89]
-    @test [linreg(x4,y4)...] ≈ [3.0,0.5] atol=10e-3
-end
-
 @testset "diag" begin
     A = Matrix(1.0I, 4, 4)
     @test diag(A) == fill(1, 4)

stdlib/Markdown/test/runtests.jl

@@ -427,20 +427,20 @@ end
 
 ref(x) = Reference(x)
 
-ref(mean)
+ref(sum)
 
 show(io::IO, m::MIME"text/plain", r::Reference) =
     print(io, "$(r.ref) (see Julia docs)")
 
-mean_ref = md"Behaves like $(ref(mean))"
-@test plain(mean_ref) == "Behaves like mean (see Julia docs)\n"
-@test html(mean_ref) == "<p>Behaves like mean &#40;see Julia docs&#41;</p>\n"
+sum_ref = md"Behaves like $(ref(sum))"
+@test plain(sum_ref) == "Behaves like sum (see Julia docs)\n"
+@test html(sum_ref) == "<p>Behaves like sum &#40;see Julia docs&#41;</p>\n"
 
 show(io::IO, m::MIME"text/html", r::Reference) =
     Markdown.withtag(io, :a, :href=>"test") do
         Markdown.htmlesc(io, Markdown.plaininline(r))
     end
-@test html(mean_ref) == "<p>Behaves like <a href=\"test\">mean &#40;see Julia docs&#41;</a></p>\n"
+@test html(sum_ref) == "<p>Behaves like <a href=\"test\">sum &#40;see Julia docs&#41;</a></p>\n"
 
 @test md"""
 ````julia

stdlib/Pkg/src/GraphType.jl

@@ -1422,7 +1422,7 @@ function prune_graph!(graph::Graph)
 
     # Done
 
-    log_event_global!(graph, "pruned graph — stats (n. of packages, mean connectivity): before = ($np,$(mean(spp))) after = ($new_np,$(mean(new_spp)))")
+    log_event_global!(graph, "pruned graph — stats (n. of packages, mean connectivity): before = ($np,$(sum(spp)/length(spp))) after = ($new_np,$(sum(new_spp)/length(new_spp)))")
 
     # Replace old data with new
     data.pkgs = new_pkgs

stdlib/REPL/src/docview.jl

@@ -378,7 +378,7 @@ function fuzzyscore(needle, haystack)
     score += (acro ? 2 : 1)*length(is) # Matched characters
     score -= 2(length(needle)-length(is)) # Missing characters
     !acro && (score -= avgdistance(is)/10) # Contiguous
-    !isempty(is) && (score -= mean(is)/100) # Closer to beginning
+    !isempty(is) && (score -= sum(is)/length(is)/100) # Closer to beginning
     return score
 end