Use 5-argument mul! instead of mulαβ!

.travis.yml

@@ -3,7 +3,6 @@ os:
   - linux
   - osx
 julia:
-  - 1.2
   - 1.3
 codecov: true
 notifications:
@@ -15,7 +14,7 @@ jobs:
   include:
     - stage: "Documentation"
       os: linux
-      julia: 1.2
+      julia: 1.3
       script:
         - julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd()));
                                                Pkg.instantiate()'

Project.toml

@@ -39,7 +39,7 @@ StatsFuns = "0.8, 0.9"
 StatsModels = "0.6"
 Tables = "0.2"
 TypedTables = "1"
-julia = "1.2"
+julia = "1.3"
 
 [extras]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

README.md

@@ -2,7 +2,7 @@
 
 |**Documentation**|**Citation**|**Build Status**|**Code Coverage**|
 |:-:|:-:|:-:|:-:|
-|[![][docs-stable-img]][docs-stable-url] [![][docs-latest-img]][docs-latest-url] | [![][doi-img]][doi-url] | [![][travis-img]][travis-url] [![][appveyor-img]][appveyor-url] | [![][coveralls-img]][coveralls-url] [![][codecov-img]][codecov-url]|
+|[![][docs-stable-img]][docs-stable-url] [![][docs-latest-img]][docs-latest-url] | [![][doi-img]][doi-url] | [![][travis-img]][travis-url] [![][appveyor-img]][appveyor-url] | [![][codecov-img]][codecov-url]|
 
 [doi-img]: https://zenodo.org/badge/9106942.svg
 [doi-url]: https://zenodo.org/badge/latestdoi/9106942
@@ -19,9 +19,6 @@
 [appveyor-img]: https://ci.appveyor.com/api/projects/status/github/JuliaStats/MixedModels.jl?svg=true
 [appveyor-url]: https://ci.appveyor.com/project/JuliaStats/mixedmodels-jl
 
-[coveralls-img]: https://coveralls.io/repos/github/JuliaStats/MixedModels.jl/badge.svg?branch=master
-[coveralls-url]: https://coveralls.io/github/JuliaStats/MixedModels.jl?branch=master
-
 [codecov-img]: https://codecov.io/github/JuliaStats/MixedModels.jl/badge.svg?branch=master
 [codecov-url]: https://codecov.io/github/JuliaStats/MixedModels.jl?branch=master
 

appveyor.yml

@@ -1,6 +1,6 @@
 environment:
   matrix:
-  - julia_version: 1.2
+  - julia_version: 1.3
   - julia_version: nightly
 
 platform:

src/MixedModels.jl

@@ -116,7 +116,6 @@ include("randomeffectsterm.jl")
 include("linearmixedmodel.jl")
 include("gausshermite.jl")
 include("generalizedlinearmixedmodel.jl")
-include("mixed.jl")
 include("linalg/statschol.jl")
 include("linalg/cholUnblocked.jl")
 include("linalg/rankUpdate.jl")

src/arraytypes.jl

@@ -1,21 +1,21 @@
-using StaticArrays, SparseArrays, LinearAlgebra
-
 """
     UniformBlockDiagonal{T}
 
 Homogeneous block diagonal matrices.  `k` diagonal blocks each of size `m×m`
 """
 struct UniformBlockDiagonal{T} <: AbstractMatrix{T}
-    data::Array{T, 3}
+    data::Array{T,3}
     facevec::Vector{SubArray{T,2,Array{T,3}}}
 end
 
 function UniformBlockDiagonal(dat::Array{T,3}) where {T}
-    UniformBlockDiagonal(dat,
-        SubArray{T,2,Array{T,3}}[view(dat,:,:,i) for i in 1:size(dat, 3)])
+    UniformBlockDiagonal(
+        dat,
+        SubArray{T,2,Array{T,3}}[view(dat, :, :, i) for i = 1:size(dat, 3)],
+    )
 end
 
-function Base.copyto!(dest::UniformBlockDiagonal{T}, src::UniformBlockDiagonal{T}) where{T}
+function Base.copyto!(dest::UniformBlockDiagonal{T}, src::UniformBlockDiagonal{T}) where {T}
     sdat = src.data
     ddat = dest.data
     size(ddat) == size(sdat) || throw(DimensionMismatch(""))
@@ -28,13 +28,13 @@ function Base.copyto!(dest::Matrix{T}, src::UniformBlockDiagonal{T}) where {T}
     fill!(dest, zero(T))
     sdat = src.data
     m, n, l = size(sdat)
-    for k in 1:l
+    for k = 1:l
         ioffset = (k - 1) * m
         joffset = (k - 1) * n
-        for j in 1:n
+        for j = 1:n
             jind = joffset + j
-            for i in 1:m
-                dest[ioffset + i, jind] = sdat[i,j,k]
+            for i = 1:m
+                dest[ioffset+i, jind] = sdat[i, j, k]
             end
         end
     end
@@ -45,7 +45,7 @@ function Base.getindex(A::UniformBlockDiagonal{T}, i::Int, j::Int) where {T}
     Ad = A.data
     m, n, l = size(Ad)
     (0 < i ≤ l * m && 0 < j ≤ l * n) ||
-        throw(IndexError("attempt to access $(l*m) × $(l*n) array at index [$i, $j]"))
+    throw(IndexError("attempt to access $(l*m) × $(l*n) array at index [$i, $j]"))
     iblk, ioffset = divrem(i - 1, m)
     jblk, joffset = divrem(j - 1, n)
     iblk == jblk ? Ad[ioffset+1, joffset+1, iblk+1] : zero(T)
@@ -54,15 +54,15 @@ end
 function LinearAlgebra.Matrix(A::UniformBlockDiagonal{T}) where {T}
     Ad = A.data
     m, n, l = size(Ad)
-    mat = zeros(T, (m*l, n*l))
+    mat = zeros(T, (m * l, n * l))
     @inbounds for k = 0:(l-1)
         kp1 = k + 1
         km = k * m
         kn = k * n
         for j = 1:n
             knpj = kn + j
             for i = 1:m
-                mat[km + i, knpj] = Ad[i, j, kp1]
+                mat[km+i, knpj] = Ad[i, j, kp1]
             end
         end
     end
@@ -81,7 +81,10 @@ A `SparseMatrixCSC` whose nonzeros form blocks of rows or columns or both.
 
 # Members
 * `cscmat`: `SparseMatrixCSC{Tv, Int32}` representation for general calculations
-* `blkpattern`: `SparseMatrixCSC{Bool,Int32}` pattern of blocks of size (S,P)
+* `nzasmat`: nonzeros of `cscmat` as a dense matrix
+* `colblkptr`: pattern of blocks of columns
+
+The only time these are created are as products of `ReMat`s.
 """
 mutable struct BlockedSparse{T,S,P} <: AbstractMatrix{T}
     cscmat::SparseMatrixCSC{T,Int32}
@@ -102,7 +105,8 @@ SparseArrays.sparse(A::BlockedSparse) = A.cscmat
 SparseArrays.nnz(A::BlockedSparse) = nnz(A.cscmat)
 
 function Base.copyto!(L::BlockedSparse{T}, A::SparseMatrixCSC{T}) where {T}
-    size(L) == size(A) && nnz(L) == nnz(A) || throw(DimensionMismatch("size(L) ≠ size(A) or nnz(L) ≠ nnz(A"))
+    size(L) == size(A) && nnz(L) == nnz(A) ||
+    throw(DimensionMismatch("size(L) ≠ size(A) or nnz(L) ≠ nnz(A"))
     copyto!(nonzeros(L.cscmat), nonzeros(A))
     L
 end

src/femat.jl

@@ -55,7 +55,7 @@ Base.size(A::FeMat, i) = size(A.wtx, i)
 Base.copyto!(A::FeMat{T}, src::AbstractVecOrMat{T}) where {T} = copyto!(A.x, src)
 
 *(adjA::Adjoint{T,<:FeMat{T}}, B::FeMat{T}) where {T} =
-    fullrankwtx(adjA.parent)'fullrankwtx(B)
+    fullrankwtx(adjA.parent)' * fullrankwtx(B)
 
 LinearAlgebra.mul!(R::StridedVecOrMat{T}, A::FeMat{T}, B::StridedVecOrMat{T}) where {T} =
     mul!(R, A.x, B)

src/gausshermite.jl

@@ -1,5 +1,3 @@
-using StaticArrays, LinearAlgebra
-
 """
     GaussHermiteQuadrature
 
@@ -28,8 +26,8 @@ gn5 = GHnorm(5)
 sum(@. abs2(σ*gn5.z + μ)*gn5.w) # E[X^2] where X ∼ N(μ, σ)
 ```
 
-For evaluation of the log-likelihood of a GLMM the integral to evaluate for each level of the grouping
-factor is approximately Gaussian shaped.
+For evaluation of the log-likelihood of a GLMM the integral to evaluate for each level of
+the grouping factor is approximately Gaussian shaped.
 """
 GaussHermiteQuadrature
 """
@@ -40,18 +38,20 @@ A struct with 2 SVector{K,Float64} members
 - `wt`: Gauss-Hermite weights normalized to sum to unity
 """
 struct GaussHermiteNormalized{K}
-    z::SVector{K, Float64}
+    z::SVector{K,Float64}
     w::SVector{K,Float64}
 end
 function GaussHermiteNormalized(k::Integer)
     ev = eigen(SymTridiagonal(zeros(k), sqrt.(1:k-1)))
-    w = abs2.(ev.vectors[1,:])
+    w = abs2.(ev.vectors[1, :])
     GaussHermiteNormalized(
         SVector{k}((ev.values .- reverse(ev.values)) ./ 2),
-        SVector{k}(LinearAlgebra.normalize((w .+ reverse(w)) ./ 2, 1)))
+        SVector{k}(LinearAlgebra.normalize((w .+ reverse(w)) ./ 2, 1)),
+    )
 end
 
-Base.iterate(g::GaussHermiteNormalized{K}, i=1) where {K} = (K < i ? nothing : ((z = g.z[i], w = g.w[i]), i + 1))
+Base.iterate(g::GaussHermiteNormalized{K}, i = 1) where {K} =
+    (K < i ? nothing : ((z = g.z[i], w = g.w[i]), i + 1))
 
 Base.length(g::GaussHermiteNormalized{K}) where {K} = K
 
@@ -61,10 +61,13 @@ Base.length(g::GaussHermiteNormalized{K}) where {K} = K
 Memoized values of `GHnorm`{@ref} stored as a `Dict{Int,GaussHermiteNormalized}`
 """
 const GHnormd = Dict{Int,GaussHermiteNormalized}(
-    1 => GaussHermiteNormalized(SVector{1}(0.),SVector{1}(1.)),
-    2 => GaussHermiteNormalized(SVector{2}(-1.0,1.0),SVector{2}(0.5,0.5)),
-    3 => GaussHermiteNormalized(SVector{3}(-sqrt(3),0.,sqrt(3)),SVector{3}(1/6,2/3,1/6))
-    )
+    1 => GaussHermiteNormalized(SVector{1}(0.0), SVector{1}(1.0)),
+    2 => GaussHermiteNormalized(SVector{2}(-1.0, 1.0), SVector{2}(0.5, 0.5)),
+    3 => GaussHermiteNormalized(
+        SVector{3}(-sqrt(3), 0.0, sqrt(3)),
+        SVector{3}(1 / 6, 2 / 3, 1 / 6),
+    ),
+)
 
 """
     GHnorm(k::Int)
@@ -74,7 +77,8 @@ Return the (unique) GaussHermiteNormalized{k} object.
 The function values are stored (memoized) when first evaluated.  Subsequent evaluations
 for the same `k` have very low overhead.
 """
-GHnorm(k::Int) = get!(GHnormd, k) do
-    GaussHermiteNormalized(k)
-end
+GHnorm(k::Int) =
+    get!(GHnormd, k) do
+        GaussHermiteNormalized(k)
+    end
 GHnorm(k) = GHnorm(Int(k))