Merge branch 'main' into pa/fma

NEWS.md

@@ -1,3 +1,8 @@
+MixedModels v4.22.4 Release Notes
+==============================
+* Switch to explicit imports from all included packages (i.e. replace `using Foo` by `using Foo: Foo, bar, baz`) [#748]
+* Reset parameter values before a `deepcopy` in a test (doesn't change test result) [#744]
+
 MixedModels v4.22.3 Release Notes
 ==============================
 * Comment out calls to `@debug` [#733]
@@ -490,3 +495,5 @@ Package dependencies
 [#715]: https://github.com/JuliaStats/MixedModels.jl/issues/715
 [#717]: https://github.com/JuliaStats/MixedModels.jl/issues/717
 [#733]: https://github.com/JuliaStats/MixedModels.jl/issues/733
+[#744]: https://github.com/JuliaStats/MixedModels.jl/issues/744
+[#748]: https://github.com/JuliaStats/MixedModels.jl/issues/748

Project.toml

@@ -1,7 +1,7 @@
 name = "MixedModels"
 uuid = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
 author = ["Phillip Alday <[email protected]>", "Douglas Bates <[email protected]>", "Jose Bayoan Santiago Calderon <[email protected]>"]
-version = "4.22.3"
+version = "4.22.4"
 
 [deps]
 Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"
@@ -36,6 +36,7 @@ BSplineKit = "0.14, 0.15, 0.16, 0.17"
 DataAPI = "1"
 DataFrames = "1"
 Distributions = "0.21, 0.22, 0.23, 0.24, 0.25"
+ExplicitImports = "1.3"
 GLM = "1.8.2"
 InteractiveUtils = "1"
 JSON3 = "1"
@@ -65,10 +66,11 @@ julia = "1.8"
 [extras]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Aqua", "DataFrames", "InteractiveUtils", "StableRNGs", "Suppressor", "Test"]
+test = ["Aqua", "DataFrames", "ExplicitImports", "InteractiveUtils", "StableRNGs", "Suppressor", "Test"]

src/MixedModels.jl

@@ -1,35 +1,47 @@
 module MixedModels
 
-using Arrow
-using BSplineKit
-using DataAPI
-using Distributions
-using GLM
-using JSON3
-using LinearAlgebra
-using Markdown
-using NLopt
-using Random
-using PooledArrays
-using ProgressMeter
-using SparseArrays
-using StaticArrays
-using Statistics
-using StatsAPI
-using StatsBase
-using StatsModels
-using StructTypes
-using Tables
-using TypedTables
-
-using LinearAlgebra: BlasFloat, BlasReal, HermOrSym, PosDefException, copytri!
+using Arrow: Arrow
 using Base: Ryu, require_one_based_indexing
-using DataAPI: levels, refpool, refarray, refvalue
-using GLM: Link, canonicallink, linkfun, linkinv, dispersion, dispersion_parameter
+using BSplineKit: BSplineKit, BSplineOrder, Natural, Derivative, SplineInterpolation
+using BSplineKit: interpolate
+using DataAPI: DataAPI, levels, refpool, refarray, refvalue
+using Distributions: Distributions, Bernoulli, Binomial, Chisq, Distribution, Gamma
+using Distributions: InverseGaussian, Normal, Poisson, ccdf, estimate
+using GLM: GLM, GeneralizedLinearModel, IdentityLink, InverseLink, LinearModel
+using GLM: Link, LogLink, LogitLink, ProbitLink, SqrtLink
+using GLM: canonicallink, glm, linkinv, dispersion, dispersion_parameter
+using JSON3: JSON3
+using LinearAlgebra: LinearAlgebra, Adjoint, BLAS, BlasFloat, ColumnNorm
+using LinearAlgebra: Diagonal, Hermitian, HermOrSym, I, LAPACK, LowerTriangular
+using LinearAlgebra: PosDefException, SVD, SymTridiagonal, Symmetric
+using LinearAlgebra: UpperTriangular, cond, diag, diagind, dot, eigen, isdiag
+using LinearAlgebra: ldiv!, lmul!, logdet, mul!, norm, normalize, normalize!, qr
+using LinearAlgebra: rank, rdiv!, rmul!, svd, tr, tril!
+using Markdown: Markdown
 using MixedModelsDatasets: dataset, datasets
-using NLopt: Opt
-using StatsModels: TableRegressionModel
+using NLopt: NLopt, Opt, ftol_abs, ftol_rel, initial_step, maxtime, xtol_abs, xtol_rel
+using PooledArrays: PooledArrays, PooledArray
+using PrecompileTools: PrecompileTools, @setup_workload, @compile_workload
+using ProgressMeter: ProgressMeter, Progress, ProgressUnknown, finish!, next!
+using Random: Random, AbstractRNG, randn!
+using SparseArrays: SparseArrays, SparseMatrixCSC, SparseVector, dropzeros!, nnz
+using SparseArrays: nonzeros, nzrange, rowvals, sparse
+using StaticArrays: StaticArrays, SVector
+using Statistics: Statistics, mean, quantile, std
+using StatsAPI: StatsAPI, aic, aicc, bic, coef, coefnames, coeftable, confint, deviance
+using StatsAPI: dof, dof_residual, fit, fit!, fitted, isfitted, islinear, leverage
+using StatsAPI: loglikelihood, meanresponse, modelmatrix, nobs, predict, r2, residuals
+using StatsAPI: response, responsename, stderror, vcov, weights
+using StatsBase: StatsBase, CoefTable, model_response, summarystats
 using StatsFuns: log2π, normccdf
+using StatsModels: StatsModels, AbstractContrasts, AbstractTerm, CategoricalTerm
+using StatsModels: ConstantTerm, DummyCoding, EffectsCoding, FormulaTerm, FunctionTerm
+using StatsModels: HelmertCoding, HypothesisCoding, InteractionTerm, InterceptTerm
+using StatsModels: MatrixTerm, SeqDiffCoding, TableRegressionModel, Term
+using StatsModels: apply_schema, drop_term, formula, modelcols, term, @formula
+using StructTypes: StructTypes
+using Tables: Tables, columntable, rows
+using TypedTables: TypedTables, DictTable, FlexTable, Table
 
 export @formula,
     AbstractReMat,
@@ -77,6 +89,8 @@ export @formula,
     condVar,
     condVartables,
     confint,
+    dataset,
+    datasets,
     deviance,
     dispersion,
     dispersion_parameter,
@@ -190,8 +204,6 @@ include("mimeshow.jl")
 include("serialization.jl")
 include("profile/profile.jl")
 
-using PrecompileTools
-
 @setup_workload begin
     # Putting some things in `setup` can reduce the size of the
     # precompile file and potentially make loading faster.

test/matrixterm.jl

@@ -42,7 +42,11 @@ end
         @test typeof(X.x) <: SparseMatrixCSC
         @test X.rank == 28
         @test X.cnames == fe.cnames
-        m1 = fit!(LinearMixedModel(collect(m.y), X, deepcopy(m.reterms), m.formula); progress=false)
+        m1 = LinearMixedModel(collect(m.y), X, deepcopy(m.reterms), m.formula)
+        # because of the way the initial values are calculated
+        # m1.optsum.initial == m.optsum.final at this point
+        copyto!(m1.optsum.initial, m.optsum.initial)
+        fit!(m1; progress=false)
         @test isapprox(m1.θ, m.θ, rtol = 1.0e-5)
     end
 

test/runtests.jl

@@ -1,4 +1,5 @@
 using Aqua
+using ExplicitImports
 using GLM
 using MixedModels
 using Test
@@ -18,6 +19,11 @@ import LinearAlgebra: BLAS
                   piracies=(;treat_as_own=[GLM.wrkresp!, Base.:|]))
 end
 
+@testset "ExplicitImports" begin
+    @test check_no_implicit_imports(MixedModels) === nothing
+    @test check_no_stale_explicit_imports(MixedModels) === nothing
+end
+
 include("utilities.jl")
 include("misc.jl")
 include("pivot.jl")