Package evaluation of QuasiCopula on Julia 1.10.8 (92f03a4775*) started at 2025-02-25T13:53:37.747 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 4.95s ################################################################################ # Installation # Installing QuasiCopula... Resolving package versions... Installed Ipopt ─ v0.8.0 Updating `~/.julia/environments/v1.10/Project.toml` [c47b6ae2] + QuasiCopula v0.1.1 Updating `~/.julia/environments/v1.10/Manifest.toml` ⌅ [14f7f29c] + AMD v0.4.0 [621f4979] + AbstractFFTs v1.5.0 ⌅ [1520ce14] + AbstractTrees v0.3.4 [79e6a3ab] + Adapt v4.2.0 [66dad0bd] + AliasTables v1.1.3 [4fba245c] + ArrayInterface v7.18.0 [6e4b80f9] + BenchmarkTools v1.6.0 [b99e7846] + BinaryProvider v0.5.10 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] + CPUSummary v0.2.6 [d360d2e6] + ChainRulesCore v1.25.1 [fb6a15b2] + CloseOpenIntervals v0.1.13 ⌅ [523fee87] + CodecBzip2 v0.7.2 [944b1d66] + CodecZlib v0.7.8 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.16.0 ⌅ [f65535da] + Convex v0.14.18 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.1.1 ⌅ [717857b8] + DSP v0.7.10 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.7.0 [864edb3b] + DataStructures v0.18.20 [e2d170a0] + DataValueInterfaces v1.0.0 [31c24e10] + Distributions v0.25.117 [ffbed154] + DocStringExtensions v0.9.3 [7a1cc6ca] + FFTW v1.8.1 [1a297f60] + FillArrays v1.13.0 [38e38edf] + GLM v1.9.0 [3e5b6fbb] + HostCPUFeatures v0.1.17 [615f187c] + IfElse v0.1.1 [842dd82b] + InlineStrings v1.4.3 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 ⌅ [b6b21f68] + Ipopt v0.8.0 ⌅ [92d709cd] + IrrationalConstants v0.1.1 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.0 [682c06a0] + JSON v0.21.4 ⌅ [40e66cde] + LDLFactorizations v0.8.2 [b964fa9f] + LaTeXStrings v1.4.0 [10f19ff3] + LayoutPointers v0.1.17 [2ab3a3ac] + LogExpFunctions v0.3.29 [bdcacae8] + LoopVectorization v0.12.171 [d125e4d3] + ManualMemory v0.1.8 ⌅ [b8f27783] + MathOptInterface v0.10.9 [fdba3010] + MathProgBase v0.7.8 [e1d29d7a] + Missings v1.2.0 ⌅ [d8a4904e] + MutableArithmetics v0.3.3 [6fe1bfb0] + OffsetArrays v1.15.0 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.32 [69de0a69] + Parsers v2.8.1 [1d0040c9] + PolyesterWeave v0.2.2 ⌃ [f27b6e38] + Polynomials v3.2.8 [2dfb63ee] + PooledArrays v1.4.3 [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [08abe8d2] + PrettyTables v2.4.0 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [c47b6ae2] + QuasiCopula v0.1.1 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.0 ⌅ [79098fc4] + Rmath v0.7.1 [94e857df] + SIMDTypes v0.1.0 [476501e8] + SLEEFPirates v0.6.43 [91c51154] + SentinelArrays v1.4.8 [1277b4bf] + ShiftedArrays v2.0.0 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.0 [aedffcd0] + Static v1.1.1 [0d7ed370] + StaticArrayInterface v1.8.0 [82ae8749] + StatsAPI v1.7.0 ⌅ [2913bbd2] + StatsBase v0.33.21 ⌅ [4c63d2b9] + StatsFuns v0.9.18 [3eaba693] + StatsModels v0.7.4 [892a3eda] + StringManipulation v0.4.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [8290d209] + ThreadingUtilities v0.5.2 ⌅ [c751599d] + ToeplitzMatrices v0.7.1 ⌅ [3bb67fe8] + TranscodingStreams v0.9.13 [3a884ed6] + UnPack v1.0.2 [3d5dd08c] + VectorizationBase v0.21.71 [ae81ac8f] + ASL_jll v0.1.3+0 [6e34b625] + Bzip2_jll v1.0.9+0 [f5851436] + FFTW_jll v3.3.10+3 [1d5cc7b8] + IntelOpenMP_jll v2025.0.4+0 ⌅ [9cc047cb] + Ipopt_jll v3.13.4+2 [d00139f3] + METIS_jll v5.1.3+0 [856f044c] + MKL_jll v2025.0.1+1 ⌅ [d7ed1dd3] + MUMPS_seq_jll v5.2.1+4 ⌅ [656ef2d0] + OpenBLAS32_jll v0.3.24+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 ⌅ [f50d1b31] + Rmath_jll v0.4.3+0 [1317d2d5] + oneTBB_jll v2022.0.0+0 [0dad84c5] + ArgTools v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [ade2ca70] + Dates [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [9fa8497b] + Future [b77e0a4c] + InteractiveUtils [4af54fe1] + LazyArtifacts [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.10.0 [de0858da] + Printf [9abbd945] + Profile [3fa0cd96] + REPL [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.2+1 [14a3606d] + MozillaCACerts_jll v2023.1.10 [4536629a] + OpenBLAS_jll v0.3.23+4 [05823500] + OpenLibm_jll v0.8.1+4 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Building Ipopt → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/539b23ab8fb86c6cc3e8cacaeb1f784415951be5/build.log` Installation completed after 9.09s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 117.59s ################################################################################ # Testing # Testing QuasiCopula Status `/tmp/jl_TwKiJX/Project.toml` [6e4b80f9] BenchmarkTools v1.6.0 ⌅ [f65535da] Convex v0.14.18 [a93c6f00] DataFrames v1.7.0 [31c24e10] Distributions v0.25.117 [7a1cc6ca] FFTW v1.8.1 [38e38edf] GLM v1.9.0 ⌅ [b6b21f68] Ipopt v0.8.0 [bdcacae8] LoopVectorization v0.12.171 [fdba3010] MathProgBase v0.7.8 [c47b6ae2] QuasiCopula v0.1.1 [ce6b1742] RDatasets v0.7.7 [189a3867] Reexport v1.2.2 [276daf66] SpecialFunctions v2.5.0 ⌅ [4c63d2b9] StatsFuns v0.9.18 ⌅ [c751599d] ToeplitzMatrices v0.7.1 [37e2e46d] LinearAlgebra [9a3f8284] Random [10745b16] Statistics v1.10.0 [8dfed614] Test Status `/tmp/jl_TwKiJX/Manifest.toml` ⌅ [14f7f29c] AMD v0.4.0 [621f4979] AbstractFFTs v1.5.0 ⌅ [1520ce14] AbstractTrees v0.3.4 [79e6a3ab] Adapt v4.2.0 [66dad0bd] AliasTables v1.1.3 [4fba245c] ArrayInterface v7.18.0 [6e4b80f9] BenchmarkTools v1.6.0 [b99e7846] BinaryProvider v0.5.10 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] CPUSummary v0.2.6 [336ed68f] CSV v0.10.15 [324d7699] CategoricalArrays v0.10.8 [d360d2e6] ChainRulesCore v1.25.1 [fb6a15b2] CloseOpenIntervals v0.1.13 ⌅ [523fee87] CodecBzip2 v0.7.2 [944b1d66] CodecZlib v0.7.8 [f70d9fcc] CommonWorldInvalidations v1.0.0 [34da2185] Compat v4.16.0 ⌅ [f65535da] Convex v0.14.18 [adafc99b] CpuId v0.3.1 [a8cc5b0e] Crayons v4.1.1 ⌅ [717857b8] DSP v0.7.10 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.20 [e2d170a0] DataValueInterfaces v1.0.0 [31c24e10] Distributions v0.25.117 [ffbed154] DocStringExtensions v0.9.3 [e2ba6199] ExprTools v0.1.10 [7a1cc6ca] FFTW v1.8.1 [5789e2e9] FileIO v1.16.6 [48062228] FilePathsBase v0.9.23 [1a297f60] FillArrays v1.13.0 [38e38edf] GLM v1.9.0 [3e5b6fbb] HostCPUFeatures v0.1.17 [615f187c] IfElse v0.1.1 [842dd82b] InlineStrings v1.4.3 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 ⌅ [b6b21f68] Ipopt v0.8.0 ⌅ [92d709cd] IrrationalConstants v0.1.1 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.0 [682c06a0] JSON v0.21.4 ⌅ [40e66cde] LDLFactorizations v0.8.2 [b964fa9f] LaTeXStrings v1.4.0 [10f19ff3] LayoutPointers v0.1.17 [2ab3a3ac] LogExpFunctions v0.3.29 [bdcacae8] LoopVectorization v0.12.171 [d125e4d3] ManualMemory v0.1.8 ⌅ [b8f27783] MathOptInterface v0.10.9 [fdba3010] MathProgBase v0.7.8 [e1d29d7a] Missings v1.2.0 [78c3b35d] Mocking v0.8.1 ⌅ [d8a4904e] MutableArithmetics v0.3.3 [6fe1bfb0] OffsetArrays v1.15.0 [bac558e1] OrderedCollections v1.8.0 [90014a1f] PDMats v0.11.32 [69de0a69] Parsers v2.8.1 [1d0040c9] PolyesterWeave v0.2.2 ⌃ [f27b6e38] Polynomials v3.2.8 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [08abe8d2] PrettyTables v2.4.0 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 [c47b6ae2] QuasiCopula v0.1.1 ⌅ [df47a6cb] RData v0.8.3 [ce6b1742] 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FFTW_jll v3.3.10+3 [1d5cc7b8] IntelOpenMP_jll v2025.0.4+0 ⌅ [9cc047cb] Ipopt_jll v3.13.4+2 [d00139f3] METIS_jll v5.1.3+0 [856f044c] MKL_jll v2025.0.1+1 ⌅ [d7ed1dd3] MUMPS_seq_jll v5.2.1+4 ⌅ [656ef2d0] OpenBLAS32_jll v0.3.24+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 ⌅ [f50d1b31] Rmath_jll v0.4.3+0 [1317d2d5] oneTBB_jll v2022.0.0+0 [0dad84c5] ArgTools v1.1.1 [56f22d72] Artifacts [2a0f44e3] Base64 [ade2ca70] Dates [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching [9fa8497b] Future [b77e0a4c] InteractiveUtils [4af54fe1] LazyArtifacts [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 [8f399da3] Libdl [37e2e46d] LinearAlgebra [56ddb016] Logging [d6f4376e] Markdown [a63ad114] Mmap [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.10.0 [de0858da] Printf [9abbd945] Profile [3fa0cd96] REPL [9a3f8284] Random [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization [6462fe0b] Sockets [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test [cf7118a7] UUIDs [4ec0a83e] Unicode [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.4.0+0 [e37daf67] LibGit2_jll v1.6.4+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.2+1 [14a3606d] MozillaCACerts_jll v2023.1.10 [4536629a] OpenBLAS_jll v0.3.23+4 [05823500] OpenLibm_jll v0.8.1+4 [bea87d4a] SuiteSparse_jll v7.2.1+1 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... WARNING: Method definition hcat(Union{Number, AbstractArray{T, N} where N where T}...) in module Base at abstractarray.jl:2007 overwritten in module Convex at /home/pkgeval/.julia/packages/Convex/uI27T/src/atoms/affine/stack.jl:117. ERROR: Method overwriting is not permitted during Module precompilation. Use `__precompile__(false)` to opt-out of precompilation. WARNING: Method definition hcat(Union{Number, AbstractArray{T, N} where N where T}...) in module Base at abstractarray.jl:2007 overwritten in module Convex at /home/pkgeval/.julia/packages/Convex/uI27T/src/atoms/affine/stack.jl:117. ERROR: Method overwriting is not permitted during Module precompilation. Use `__precompile__(false)` to opt-out of precompilation. WARNING: Method definition hcat(Union{Number, AbstractArray{T, N} where N where T}...) in module Base at abstractarray.jl:2007 overwritten in module Convex at /home/pkgeval/.julia/packages/Convex/uI27T/src/atoms/affine/stack.jl:117. WARNING: Method definition vcat(Union{Number, AbstractArray{T, N} where N where T}...) in module Base at abstractarray.jl:2003 overwritten in module Convex at /home/pkgeval/.julia/packages/Convex/uI27T/src/atoms/affine/stack.jl:123. WARNING: method definition for pmf_copula! at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/src/generate_random_deviates/discrete_rand.jl:86 declares type variable T but does not use it. WARNING: method definition for rand at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/src/generate_random_deviates/discrete_rand.jl:128 declares type variable T but does not use it. WARNING: method definition for rand at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/src/generate_random_deviates/discrete_rand.jl:149 declares type variable T but does not use it. [ Info: sample 10000 points for the Distributions.Normal{Float64}(μ=0.0, σ=1.0) distribution 0.037239 seconds sample mean = -0.009060170484946875; theoretical mean = 0.0 sample var = 1.6473793620131703; theoretical var = 1.6666666666666665 [ Info: sample 10000 points for the Distributions.Gamma{Float64}(α=2.5, θ=1.0) distribution 0.090917 seconds sample mean = 3.1426195902066834; theoretical mean = 3.1666666666666665 sample var = 5.615444014514515; theoretical var = 5.722222222222223 [ Info: sample 10000 points for the Distributions.Exponential{Float64}(θ=3.0) distribution 0.043595 seconds sample mean = 4.940083193165901; theoretical mean = 5.0 sample var = 28.430643602254055; theoretical var = 29.0 [ Info: sample 10000 points for the Distributions.Poisson{Float64}(λ=5.0) distribution 0.022876 seconds (30.00 k allocations: 6.409 MiB) sample mean = 5.2631; theoretical mean = 5.333333333333333 sample var = 8.457324122412246; theoretical var = 8.555555555555557 [ Info: sample 10000 points for the Distributions.Binomial{Float64}(n=30, p=0.1) distribution using the Bisection method. 0.063442 seconds (30.00 k allocations: 5.188 MiB, 31.43% gc time) sample mean = 3.229; theoretical mean = 3.2666666666666666 sample var = 4.520011001100108; theoretical var = 4.582222222222223 [ Info: sample 10000 points for the Distributions.Geometric{Float64}(p=0.2) distribution. 0.082412 seconds (30.00 k allocations: 21.667 MiB) sample mean = 6.8839; theoretical mean = 7.0 sample var = 63.67718850885089; theoretical var = 64.66666666666663 [ Info: sample 10000 points for the Distributions.Geometric{Float64}(p=0.5) distribution. 0.027739 seconds (30.00 k allocations: 8.545 MiB) sample mean = 1.962; theoretical mean = 2.0 sample var = 6.567412741274121; theoretical var = 6.666666666666666 [ Info: sample 10000 points for the Distributions.NegativeBinomial{Float64}(r=5.0, p=0.5) distribution. 0.121019 seconds (30.00 k allocations: 10.376 MiB, 14.08% gc time) sample mean = 5.9328; theoretical mean = 6.0 sample var = 19.724056565656543; theoretical var = 20.0 [ Info: sample 10000 points for the Distributions.Bernoulli{Float64}(p=0.8) distribution 0.000478 seconds sample mean = 0.6021; theoretical mean = 0.6000000000000001 sample var = 0.23959954995499774; theoretical var = 0.23999999999999966 [ Info: sample 10000 points for the conditional Normal distribution 0.040683 seconds sample mean = 5.027355195622659; theoretical mean = 5.0268812449603475 sample var = 0.04223199979704868; theoretical var = 0.042506950220051465 [ Info: sample 10000 points for the conditional Normal distribution 0.040659 seconds sample mean = 5.0423662741375255; theoretical mean = 5.041899564380567 sample var = 0.04042091738860136; theoretical var = 0.04068226288724475 [ Info: sample 10000 points for the conditional Poisson distribution 0.022818 seconds (30.00 k allocations: 6.409 MiB) sample mean = 4.7091; theoretical mean = 4.722222222222221 sample var = 5.068784068406837; theoretical var = 5.041887125220466 [ Info: sample 10000 points for the conditional Poisson distribution 0.022833 seconds (30.00 k allocations: 6.409 MiB) sample mean = 4.5329; theoretical mean = 4.55621301775148 sample var = 4.64358194819481; theoretical var = 4.655124120303913 [ Info: sample 10000 points for the conditional Exponential distribution 0.039200 seconds (9 allocations: 432 bytes) sample mean = 0.24339356103507048; theoretical mean = 0.2430290027155713 sample var = 0.06376802899040843; theoretical var = 0.06181920911362257 [ Info: sample 10000 points for the conditional Poisson distribution 0.006501 seconds (30.01 k allocations: 2.747 MiB) sample mean = 0.0484; theoretical mean = 0.05016712944519664 sample var = 0.047462186218621735; theoretical var = 0.04862555421512253 [ Info: sample 10000 independent vectors for the bivariate Poisson distribution Statistics.cor(Y_11, Y_12) = 0.0811921680896936 Statistics.cor(Y_21, Y_22) = -0.08998847615758022 [ Info: sample 10000 independent vectors for the normal and poisson Statistics.cor(Y_11, Y_12) = 0.0946978894603958 sample cor = 0.0946978894603958; theoretical cor = 0.08333333333333331 sample cov = 0.05475511903218344; theoretical var = 0.048104569292083475 Statistics.cor(Y_21, Y_22) = -0.08909556181080186 sample cor = -0.08909556181080186; theoretical cor = -0.08333333333333331 sample cov = -0.05127939863940834; theoretical var = -0.048104569292083475 [ Info: sample 10000 independent vectors for the mixed normal and binary bivariate sample cor = 0.08958970601525193; theoretical cor = 0.08333333333333331 sample cov = 0.009381052398876353; theoretical var = 0.009090909090909092 sample cor = -0.08142660370437514; theoretical cor = -0.08333333333333331 sample cov = -0.008369089784363291; theoretical var = -0.009090909090909092 [ Info: sample 10000 independent vectors for the mixed normal and binary bivariate sample cor = 0.08764125262391863; theoretical cor = 0.08333333333333331 sample cov = 0.009061189136466901; theoretical cov = 0.009090909090909092 sample cor = -0.09495194903230834; theoretical cor = -0.08333333333333331 sample cov = -0.00996415878713436; theoretical cov = -0.009090909090909092 Test Summary: | Pass Total Time Generating Random Deviates | 65 65 3m18.4s ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_nbVCM.jl:43 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_nbVCM.jl:46 precompiling NB VCM fit initializing β using GLM.jl gcm.β = [1.7981936658875894, 1.453911430812571, -0.6349757438588364] initializing variance components using MM-Algorithm ┌ Warning: #= /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/update_sigma_and_residuals.jl:65 =#: │ `LoopVectorization.check_args` on your inputs failed; running fallback `@inbounds @fastmath` loop instead. │ Use `warn_check_args=false`, e.g. `@turbo warn_check_args=false ...`, to disable this warning. └ @ QuasiCopula ~/.julia/packages/LoopVectorization/tIJUA/src/condense_loopset.jl:1166 gcm.θ = [2.719336278259679e-6] initializing r using Newton update Converging when tol ≤ 1.0e-6 (max block iter = 1) ****************************************************************************** This program contains Ipopt, a library for large-scale nonlinear optimization. Ipopt is released as open source code under the Eclipse Public License (EPL). For more information visit https://github.com/coin-or/Ipopt ****************************************************************************** Block iter 1 r = 9.95, logl = -655067.65, tol = 655067.6497315489 initializing β using GLM.jl gcm.β = [1.7981936658875894, 1.453911430812571, -0.6349757438588364] initializing variance components using MM-Algorithm gcm.θ = [2.719336278259679e-6] initializing r using Newton update Converging when tol ≤ 1.0e-6 (max block iter = 10) Block iter 1 r = 9.95, logl = -655067.65, tol = 655067.6497315489 Block iter 2 r = 9.97, logl = -654876.98, tol = 0.0002910696946232934 Block iter 3 r = 9.97, logl = -654870.17, tol = 1.0398734407963635e-5 fittime = 47.794813126 gcm.β = [1.7788319435024975, 1.4675578244853493, -0.6407872331078062] gcm.θ = [0.49158360528790374] gcm.r = [9.972003929744258] gcm.∇β = [1491.016269436608, 376.87246264552317, -999.4348025983231] gcm.∇θ = [-5.384643510165006] gcm.∇r = [-396.3350882977317] QuasiCopula.confint(gcm) = [1.7772152245546742 1.7804486624503209; 1.465793625455497 1.4693220235152016; -0.6426862350638202 -0.6388882311517922; 9.857237465763044 10.086770393725471; 0.49158360528790374 0.49158360528790374] mseβ = 2.3249821510196578e-7 mser = 0.0007837799497644681 mseθ = 7.083569994980196e-5 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_bernoulliVCM.jl:44 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_bernoulliVCM.jl:47 precompiling Bernoulli VCM fit initializing β using Newton's Algorithm under Independence Assumption gcm.β = [1.498527188845584, 1.2014103523960917, -0.5368234676265449] initializing variance components using MM-Algorithm gcm.θ = [0.2258952584092549] ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_glm_vc.jl:37 initializing β using Newton's Algorithm under Independence Assumption gcm.β = [1.498527188845584, 1.2014103523960917, -0.5368234676265449] initializing variance components using MM-Algorithm gcm.θ = [0.2258952584092549] This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 4 variables with only lower bounds: 1 variables with lower and upper bounds: 0 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 1.0768961e+05 0.00e+00 1.00e+02 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 Warning: Cutting back alpha due to evaluation error Warning: Cutting back alpha due to evaluation error Warning: Cutting back alpha due to evaluation error Warning: Cutting back alpha due to evaluation error 1 2.3376813e+05 0.00e+00 7.80e+02 -9.6 1.00e+02 - 1.00e+00 6.25e-02h 5 2 1.3580736e+05 0.00e+00 1.69e+02 -5.6 3.78e+00 - 5.42e-01 1.00e+00f 1 3 1.2529995e+05 0.00e+00 4.42e+02 -5.6 6.75e+00 - 1.00e+00 5.00e-01f 2 4 1.0685074e+05 0.00e+00 7.88e+01 -5.6 1.26e+00 - 1.00e+00 1.00e+00f 1 5 1.0664546e+05 0.00e+00 7.45e+00 -11.0 1.75e-01 - 1.00e+00 5.00e-01f 2 6 1.0663672e+05 0.00e+00 3.95e+00 -8.7 9.09e-02 - 1.00e+00 5.00e-01f 2 7 1.0663350e+05 0.00e+00 3.00e+00 -11.0 1.49e-02 - 1.00e+00 1.00e+00f 1 8 1.0663042e+05 0.00e+00 3.03e+00 -11.0 2.21e-02 - 1.00e+00 1.00e+00f 1 9 1.0661958e+05 0.00e+00 4.50e+00 -11.0 8.31e-02 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 1.0660407e+05 0.00e+00 2.90e+00 -11.0 6.60e-01 - 1.00e+00 2.48e-01f 3 11 1.0660343e+05 0.00e+00 5.66e-01 -11.0 3.40e-02 - 1.00e+00 1.00e+00f 1 12 1.0660317e+05 0.00e+00 2.72e-01 -11.0 2.31e-02 - 1.00e+00 1.00e+00f 1 13 1.0660315e+05 0.00e+00 1.74e-02 -11.0 3.78e-03 - 1.00e+00 1.00e+00f 1 14 1.0660315e+05 0.00e+00 8.37e-04 -11.0 3.64e-04 - 1.00e+00 1.00e+00f 1 15 1.0660315e+05 0.00e+00 1.89e-05 -11.0 1.36e-05 - 1.00e+00 1.00e+00f 1 16 1.0660315e+05 0.00e+00 1.51e-06 -11.0 2.14e-08 - 1.00e+00 1.00e+00f 1 17 1.0660315e+05 0.00e+00 7.55e-07 -11.0 1.88e-09 - 1.00e+00 5.00e-01f 2 18 1.0660315e+05 0.00e+00 9.08e-12 -11.0 9.41e-10 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 18 (scaled) (unscaled) Objective...............: 2.0935315722775176e+03 1.0660315139786649e+05 Dual infeasibility......: 9.0801494355859932e-12 4.6236348083539269e-10 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 9.9999999999999994e-12 5.0920250169379926e-10 Overall NLP error.......: 9.9999999999999994e-12 5.0920250169379926e-10 Number of objective function evaluations = 49 Number of objective gradient evaluations = 19 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.166 Total CPU secs in NLP function evaluations = 3.826 EXIT: Optimal Solution Found. fittime = 4.383713342 gcm.β = [1.7833641026916878, 1.475770986041136, -0.655496956115374] gcm.θ = [0.4827820103849778] gcm.∇β = [5.843681094575004e-11, 2.336064675034777e-10, 4.623634808353927e-10] gcm.∇θ = [-1.046299269447104e-9] get_CI(gcm) = [1.7747841318980895 1.791944073485286; 1.4651553491766423 1.48638662290563; -0.66597812555621 -0.6450157866745378; 0.43184392884902517 0.5337200919209305] mseβ = 9.305384027874865e-5 mseθ = 0.00029645916638301197 checking memory allocation for Bernoulli VCM ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_normalVCM.jl:41 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_normalVCM.jl:44 precompiling Gaussian VCM fit gcm.β = [1.779294967708583, 1.4676874710507113, -0.640973368333268] initializing dispersion using residual sum of squares gcm.τ = [93.31977926486024] initializing variance components using MM-Algorithm gcm.θ = [0.5102886707935435] ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_gaussian_vc.jl:36 gcm.β = [1.779294967708583, 1.4676874710507113, -0.640973368333268] initializing dispersion using residual sum of squares gcm.τ = [93.31977926486024] initializing variance components using MM-Algorithm gcm.θ = [0.5102886707935435] This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 5 variables with only lower bounds: 2 variables with lower and upper bounds: 0 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 -2.1734150e+05 0.00e+00 1.00e+02 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 5.2729087e+08 0.00e+00 2.49e+06 -0.4 1.00e+02 - 9.94e-01 6.25e-02h 5 2 -2.1734201e+05 0.00e+00 4.26e+01 -0.3 6.25e+00 - 1.00e+00 1.00e+00f 1 3 -2.1734209e+05 0.00e+00 1.82e+01 -2.2 1.07e-04 - 1.00e+00 1.00e+00f 1 4 -2.1734211e+05 0.00e+00 3.41e-02 -4.1 3.20e-05 - 1.00e+00 1.00e+00f 1 5 -2.1734211e+05 0.00e+00 8.99e-03 -6.0 6.15e-08 - 1.00e+00 1.00e+00f 1 6 -2.1734211e+05 0.00e+00 6.53e-03 -6.1 2.45e-08 - 1.00e+00 1.00e+00f 1 7 -2.1734211e+05 0.00e+00 6.53e-03 -6.1 4.10e-08 - 1.00e+00 1.00e+00f 1 8 -2.1734211e+05 0.00e+00 2.52e-02 -6.1 6.90e-07 - 1.00e+00 1.00e+00f 1 9 -2.1734211e+05 0.00e+00 1.09e-01 -6.1 1.29e-05 - 1.00e+00 6.25e-02h 5 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 -2.1734211e+05 0.00e+00 7.65e-02 -6.1 3.10e-06 - 1.00e+00 1.00e+00f 1 11 -2.1734211e+05 0.00e+00 1.21e-01 -6.1 8.17e-06 - 1.00e+00 1.00e+00f 1 12 -2.1734211e+05 0.00e+00 3.15e-01 -6.1 1.30e-04 - 1.00e+00 1.00e+00f 1 13 -2.1734211e+05 0.00e+00 3.05e-01 -6.1 9.24e-04 - 1.00e+00 6.25e-02f 5 14 -2.1734211e+05 0.00e+00 3.02e-01 -6.1 1.14e-04 - 1.00e+00 1.00e+00f 1 15 -2.1734211e+05 0.00e+00 9.65e-02 -6.1 1.34e-04 - 1.00e+00 5.00e-01f 2 16 -2.1734211e+05 0.00e+00 1.19e-02 -8.0 5.55e-05 - 1.00e+00 1.00e+00f 1 17 -2.1734211e+05 0.00e+00 6.73e-04 -8.1 1.73e-06 - 1.00e+00 1.00e+00f 1 18 -2.1734211e+05 0.00e+00 3.16e-03 -11.0 1.53e-06 - 1.00e+00 1.00e+00f 1 19 -2.1734211e+05 0.00e+00 8.14e-03 -11.0 7.15e-06 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 20 -2.1734211e+05 0.00e+00 2.01e-02 -11.0 7.35e-05 - 1.00e+00 1.00e+00f 1 21 -2.1734211e+05 0.00e+00 8.58e-03 -11.0 1.61e-04 - 1.00e+00 1.00e+00f 1 22 -2.1734211e+05 0.00e+00 3.50e-03 -11.0 8.39e-05 - 1.00e+00 1.25e-01f 4 23 -2.1734211e+05 0.00e+00 8.14e-04 -11.0 3.41e-07 - 1.00e+00 1.00e+00f 1 24 -2.1734211e+05 0.00e+00 6.80e-05 -11.0 8.41e-07 - 1.00e+00 1.00e+00f 1 25 -2.1734211e+05 0.00e+00 3.49e-06 -11.0 1.37e-07 - 1.00e+00 1.00e+00f 1 26 -2.1734211e+05 0.00e+00 7.05e-08 -11.0 5.10e-09 - 1.00e+00 1.00e+00f 1 27 -2.1734211e+05 0.00e+00 2.92e-09 -11.0 2.33e-11 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 27 (scaled) (unscaled) Objective...............: -3.4733495687762761e+03 -2.1734211244909657e+05 Dual infeasibility......: 2.9228398987826401e-09 1.8289440362195816e-07 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 9.9999999999999994e-12 6.2574212052508762e-10 Overall NLP error.......: 2.9228398987826401e-09 1.8289440362195816e-07 Number of objective function evaluations = 64 Number of objective gradient evaluations = 28 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.227 Total CPU secs in NLP function evaluations = 2.477 EXIT: Optimal Solution Found. fittime = 2.921078128 gcm.β = [1.7794710114173165, 1.4676426070588442, -0.6410283002773441] gcm.θ = [0.5106201385743676] gcm.τ = [99.77380648633272] gcm.∇β = [1.828944036219582e-7, 6.835287535977841e-8, -8.523213068656332e-8] gcm.∇θ = [1.0739049649544086e-9] gcm.∇τ = [-5.157963986479608e-11] QuasiCopula.confint(gcm) = [1.7793212674512355 1.7796207553833974; 1.4673685306210904 1.467916683496598; -0.6412930592783793 -0.640763541276309; 99.2298189457652 100.31779402690023; 0.455287450787558 0.5659528263611773] mseβ = 1.2521750909106832e-7 mseτ = 5.139575037376407e-10 mseθ = 0.0001127873433387709 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_poissonVCM.jl:44 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_poissonVCM.jl:47 precompiling Poisson VCM fit initializing β using Newton's Algorithm under Independence Assumption gcm.β = [1.7850688580430727, 1.464585599480782, -0.6406797990378421] initializing variance components using MM-Algorithm gcm.θ = [0.4647201796737404] ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_glm_vc.jl:37 initializing β using Newton's Algorithm under Independence Assumption gcm.β = [1.7850688580430727, 1.464585599480782, -0.6406797990378421] initializing variance components using MM-Algorithm gcm.θ = [0.4647201796737404] This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 4 variables with only lower bounds: 1 variables with lower and upper bounds: 0 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 5.7242843e+05 0.00e+00 1.00e+02 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 3.3384103e+07 0.00e+00 6.72e+04 -1.8 1.00e+02 - 1.00e+00 6.25e-02h 5 2 5.7336945e+05 0.00e+00 7.20e+02 -2.4 6.21e+00 - 1.00e+00 1.00e+00f 1 3 5.7319683e+05 0.00e+00 9.19e+02 -2.4 1.95e-01 - 1.00e+00 2.50e-01f 3 4 5.7264050e+05 0.00e+00 6.84e+02 -2.4 1.11e-02 - 1.00e+00 1.00e+00f 1 5 5.7249473e+05 0.00e+00 2.14e+02 -2.4 1.62e-02 - 1.00e+00 1.00e+00f 1 6 5.7240049e+05 0.00e+00 7.11e+01 -2.4 8.53e-03 - 1.00e+00 1.00e+00f 1 7 5.7239939e+05 0.00e+00 4.89e+01 -4.3 2.28e-03 - 1.00e+00 1.00e+00f 1 8 5.7239564e+05 0.00e+00 2.58e+00 -6.2 1.13e-03 - 1.00e+00 1.00e+00f 1 9 5.7239564e+05 0.00e+00 5.71e-01 -8.0 2.66e-05 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 5.7239564e+05 0.00e+00 1.64e-01 -9.9 4.86e-06 - 1.00e+00 1.00e+00f 1 11 5.7239564e+05 0.00e+00 1.64e-01 -10.0 2.21e-06 - 1.00e+00 1.00e+00f 1 12 5.7239563e+05 0.00e+00 5.70e-01 -11.0 5.62e-05 - 1.00e+00 1.00e+00f 1 13 5.7239563e+05 0.00e+00 1.74e+00 -11.0 4.06e-04 - 1.00e+00 1.00e+00f 1 14 5.7239558e+05 0.00e+00 3.02e+00 -11.0 3.49e-03 - 1.00e+00 1.00e+00f 1 15 5.7239598e+05 0.00e+00 2.64e+01 -11.0 1.24e-01 - 1.00e+00 6.25e-02h 5 16 5.7239553e+05 0.00e+00 3.75e+00 -11.0 1.33e-03 - 1.00e+00 1.00e+00f 1 17 5.7239552e+05 0.00e+00 1.81e+00 -11.0 8.67e-04 - 1.00e+00 1.00e+00f 1 18 5.7239552e+05 0.00e+00 9.63e-02 -11.0 1.06e-03 - 1.00e+00 1.00e+00f 1 19 5.7239552e+05 0.00e+00 4.39e-02 -11.0 1.67e-05 - 1.00e+00 5.00e-01f 2 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 20 5.7239552e+05 0.00e+00 9.67e-04 -11.0 4.60e-06 - 1.00e+00 1.00e+00f 1 21 5.7239552e+05 0.00e+00 4.63e-05 -11.0 2.14e-08 - 1.00e+00 1.00e+00f 1 22 5.7239552e+05 0.00e+00 1.78e-07 -11.0 2.73e-09 - 1.00e+00 1.00e+00f 1 23 5.7239552e+05 0.00e+00 1.36e-07 -11.0 2.87e-10 - 1.00e+00 2.50e-01f 3 24 5.7239552e+05 0.00e+00 1.28e-07 -11.0 2.33e-10 - 1.00e+00 6.25e-02f 5 25 5.7239552e+05 0.00e+00 1.12e-07 -11.0 2.18e-10 - 1.00e+00 1.25e-01f 4 26 5.7239552e+05 0.00e+00 1.05e-07 -11.0 1.91e-10 - 1.00e+00 6.25e-02h 5 27 5.7239552e+05 0.00e+00 7.85e-08 -11.0 1.79e-10 - 1.00e+00 2.50e-01f 3 28 5.7239552e+05 0.00e+00 5.89e-08 -11.0 1.34e-10 - 1.00e+00 2.50e-01f 3 29 5.7239552e+05 0.00e+00 2.94e-08 -11.0 1.01e-10 - 1.00e+00 5.00e-01f 2 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 30 5.7239552e+05 0.00e+00 9.81e-12 -11.0 5.03e-11 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 30 (scaled) (unscaled) Objective...............: 4.8442544433166577e+03 5.7239551835708006e+05 Dual infeasibility......: 9.8072841253581108e-12 1.1588254800187769e-09 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 9.9999999999999994e-12 1.1815967246451754e-09 Overall NLP error.......: 9.9999999999999994e-12 1.1815967246451754e-09 Number of objective function evaluations = 104 Number of objective gradient evaluations = 31 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.340 Total CPU secs in NLP function evaluations = 8.185 EXIT: Optimal Solution Found. fittime = 8.917028751 gcm.β = [1.7792886279911988, 1.4670879216125434, -0.6416593242271376] gcm.θ = [0.4768908232650406] gcm.∇β = [-7.530029932922844e-10, -1.158825480018777e-9, 7.792833045527914e-10] gcm.∇θ = [-2.472646531970213e-9] QuasiCopula.confint(gcm) = [1.778733150513717 1.7798441054686807; 1.4666779874251938 1.467497855799893; -0.6420759146527877 -0.6412427338014874; 0.4262221644611728 0.5275594820689085] mseβ = 8.958627277664381e-8 mseθ = 0.0005340340493675879 checking memory allocation for Poisson VCM ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_poisson_bernoulli_mixedVCM.jl:55 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/VCM/singlerun_poisson_bernoulli_mixedVCM.jl:58 precompiling Bivariate Mixed VCM fit initializing β using Newton's Algorithm under Independence Assumption gcm.β = [0.017840694760431174, 0.05621226634805315, 0.14272539945155446, -0.14748482477703126, -0.17285215063578974, 0.12284165295325236] initializing variance components using MM-Algorithm ┌ Warning: #= /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/update_sigma_and_residuals.jl:84 =#: │ `LoopVectorization.check_args` on your inputs failed; running fallback `@inbounds @fastmath` loop instead. │ Use `warn_check_args=false`, e.g. `@turbo warn_check_args=false ...`, to disable this warning. └ @ QuasiCopula ~/.julia/packages/LoopVectorization/tIJUA/src/condense_loopset.jl:1166 gcm.θ = [0.08861017222823873] ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_glm_vc.jl:37 initializing β using Newton's Algorithm under Independence Assumption gcm.β = [0.017840694760431174, 0.05621226634805315, 0.14272539945155446, -0.14748482477703126, -0.17285215063578974, 0.12284165295325236] initializing variance components using MM-Algorithm gcm.θ = [0.08861017222823873] This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 7 variables with only lower bounds: 1 variables with lower and upper bounds: 0 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 2.0140320e+04 0.00e+00 1.00e+02 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 Warning: Cutting back alpha due to evaluation error 1 2.0133010e+04 0.00e+00 4.33e+01 -7.1 1.00e+02 - 1.00e+00 4.88e-04f 12 2 2.0131345e+04 0.00e+00 6.16e+00 -3.4 1.51e-02 - 1.00e+00 1.00e+00f 1 3 2.0131191e+04 0.00e+00 4.69e+00 -5.9 2.97e-03 - 1.00e+00 1.00e+00f 1 4 2.0131015e+04 0.00e+00 2.94e+00 -7.8 8.16e-03 - 1.00e+00 1.00e+00f 1 5 2.0131010e+04 0.00e+00 2.39e+00 -11.0 3.41e-03 - 1.00e+00 5.00e-01f 2 6 2.0131002e+04 0.00e+00 2.04e-01 -11.0 9.11e-04 - 1.00e+00 1.00e+00f 1 7 2.0131001e+04 0.00e+00 4.58e-02 -11.0 6.60e-05 - 1.00e+00 1.00e+00f 1 8 2.0131001e+04 0.00e+00 2.37e-02 -11.0 2.58e-05 - 1.00e+00 1.00e+00f 1 9 2.0131001e+04 0.00e+00 1.52e-02 -11.0 2.89e-05 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 2.0131001e+04 0.00e+00 1.64e-02 -11.0 1.76e-05 - 1.00e+00 1.00e+00f 1 11 2.0131001e+04 0.00e+00 6.71e-03 -11.0 9.19e-06 - 1.00e+00 1.00e+00f 1 12 2.0131001e+04 0.00e+00 8.36e-05 -11.0 3.64e-06 - 1.00e+00 1.00e+00f 1 13 2.0131001e+04 0.00e+00 9.69e-06 -11.0 3.96e-08 - 1.00e+00 1.00e+00f 1 14 2.0131001e+04 0.00e+00 7.09e-06 -11.0 6.39e-09 - 1.00e+00 2.50e-01f 3 15 2.0131001e+04 0.00e+00 7.70e-07 -11.0 6.84e-09 - 1.00e+00 1.00e+00f 1 16 2.0131001e+04 0.00e+00 2.22e-07 -11.0 8.54e-10 - 1.00e+00 1.00e+00f 1 17 2.0131001e+04 0.00e+00 1.28e-08 -11.0 1.86e-10 - 1.00e+00 1.00e+00f 1 18 2.0131001e+04 0.00e+00 1.32e-08 -11.0 1.30e-11 - 1.00e+00 1.00e+00f 1 19 2.0131001e+04 0.00e+00 1.12e-11 -11.0 6.71e-12 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 19 (scaled) (unscaled) Objective...............: 5.4851449626800941e+03 2.0131001451079621e+04 Dual infeasibility......: 1.1247653858077148e-11 4.1279954801332761e-11 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 9.9999999999999994e-12 3.6700946990548489e-11 Overall NLP error.......: 1.1247653858077148e-11 4.1279954801332761e-11 Number of objective function evaluations = 42 Number of objective gradient evaluations = 20 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.142 Total CPU secs in NLP function evaluations = 2.068 EXIT: Optimal Solution Found. fittime = 2.549785921 gcm.β = [-0.030681317395973046, 0.058256365042874986, 0.149939845454087, -0.163109153704052, -0.19201717326298157, 0.13640591775860392] gcm.θ = [0.10603096344435475] gcm.∇β = [2.1205731615125956e-11, 3.4977964968874176e-11, 4.034994560697669e-12, 1.1430689728086918e-11, -6.932843188423021e-12, 7.717076977442616e-12] gcm.∇θ = [-3.0485425295267987e-10] QuasiCopula.confint(gcm) = [-0.0423707024350982 -0.018991932356847895; 0.04205214534381156 0.07446058474193841; 0.1346498957323019 0.1652297951758721; -0.19791902244803797 -0.128299284960066; -0.22534356168241731 -0.15869078484354582; 0.10148220764976905 0.17132962786743877; 0.09013711620109376 0.12192481068761575] mseβ = 0.00023359600173418322 mseθ = 3.6372520067143284e-5 Test Summary: | Pass Total Time VCM Covariance | 36 36 4m57.9s ┌ Warning: Assignment to `p` in soft scope is ambiguous because a global variable by the same name exists: `p` will be treated as a new local. Disambiguate by using `local p` to suppress this warning or `global p` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_nbAR.jl:54 ┌ Warning: Assignment to `vecd` in soft scope is ambiguous because a global variable by the same name exists: `vecd` will be treated as a new local. Disambiguate by using `local vecd` to suppress this warning or `global vecd` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_nbAR.jl:55 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_nbAR.jl:59 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_nbAR.jl:63 precompiling NB AR fit Initializing NegBin r to Poisson regression values initializing β using Newton's Algorithm under Independence Assumption initializing variance components using MM-Algorithm initializing r using Newton update Converging when tol ≤ 1.0e-6 (max block iter = 1) Block iter 1 r = 9.53, logl = -659901.17, tol = 659901.1660372826 Initializing NegBin r to Poisson regression values initializing β using Newton's Algorithm under Independence Assumption initializing variance components using MM-Algorithm initializing r using Newton update Converging when tol ≤ 1.0e-6 (max block iter = 5) Block iter 1 r = 9.53, logl = -659901.17, tol = 659901.1660372826 Block iter 2 r = 9.73, logl = -659853.26, tol = 7.258816648412101e-5 Block iter 3 r = 9.83, logl = -659842.7, tol = 1.6018166625496154e-5 Block iter 4 r = 9.88, logl = -659840.2, tol = 3.785610510716284e-6 fittime = 51.585565219 gcm.β = [1.7804478602034104, 1.4651229429364983, -0.64013749917524] gcm.σ2 = [0.3503885689488392] gcm.ρ = [0.4924126787494217] gcm.∇β = [25.214851175256236, 8.869082360699663, -7.808814180637369] gcm.∇σ2 = [4.433849177735093] gcm.∇ρ = [-0.4669756033527722] gcm.r = [9.906162352715347] gcm.∇r = [-384.9117805203059] QuasiCopula.confint(gcm) = [1.7793071765392392 1.7815885438675816; 1.4639060453650024 1.4663398405079942; -0.6417176226137397 -0.6385573757367403; 0.450802023551523 0.5340233339473204; 0.24242703121354855 0.4583501066841298; 9.790385070033922 10.021939635396771] mseβ = 3.2313693982531767e-6 mser = 0.008805504047918975 mseσ2 = 0.022383580301176247 mseρ = 5.7567443759477304e-5 WARNING: Method definition get_V(Any, Any) in module PkgTest at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_nbAR.jl:17 overwritten at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_bernoulliAR.jl:16. ┌ Warning: Assignment to `vecd` in soft scope is ambiguous because a global variable by the same name exists: `vecd` will be treated as a new local. Disambiguate by using `local vecd` to suppress this warning or `global vecd` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_bernoulliAR.jl:57 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_bernoulliAR.jl:63 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_bernoulliAR.jl:66 precompiling Bernoulli AR fit initializing β using Newton's Algorithm under Independence Assumption initializing variance components using MM-Algorithm ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_glm_ar_cs.jl:39 initializing β using Newton's Algorithm under Independence Assumption initializing variance components using MM-Algorithm Total number of variables............................: 5 variables with only lower bounds: 1 variables with lower and upper bounds: 1 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 Number of Iterations....: 20 (scaled) (unscaled) Objective...............: 1.9927950733029102e+03 1.2507291566136708e+05 Dual infeasibility......: 1.9826510229526204e-07 1.2443624911651074e-05 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 9.9999999999999719e-12 6.2762557644257731e-10 Overall NLP error.......: 1.9826510229526204e-07 1.2443624911651074e-05 Number of objective function evaluations = 41 Number of objective gradient evaluations = 21 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.198 Total CPU secs in NLP function evaluations = 3.683 EXIT: Optimal Solution Found. fittime = 4.201213593 gcm.β = [1.7696053343718074, 1.4572403005255137, -0.6353670960907822] gcm.σ2 = [0.5059654903997188] gcm.ρ = [0.5262999766619075] gcm.∇β = [1.2443624911651074e-5, 2.287479107909718e-6, -4.385627998093611e-6] gcm.∇σ2 = [-1.0440668374300799e-7] gcm.∇ρ = [-5.304648917126009e-6] get_CI(gcm) = [1.7612939381600994 1.7779167305835155; 1.4525357751630303 1.461944825887997; -0.6474138721259237 -0.6233203200556408; 0.48664174234493895 0.5659582109788761; 0.3404096255537902 0.6715213552456475] mseβ = 7.883748466682836e-5 mseσ2 = 3.5587075709137544e-5 mseρ = 0.0006916887724168809 checking memory allocation for Bernoulli AR WARNING: Method definition get_V(Any, Any) in module PkgTest at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_bernoulliAR.jl:16 overwritten at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_normalAR.jl:19. ┌ Warning: Assignment to `vecd` in soft scope is ambiguous because a global variable by the same name exists: `vecd` will be treated as a new local. Disambiguate by using `local vecd` to suppress this warning or `global vecd` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_normalAR.jl:48 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_normalAR.jl:54 precompiling Gaussian AR fit gcm.β = [1.7792374382746265, 1.4676487199311803, -0.6410042443969289] initializing dispersion using residual sum of squares gcm.τ = [93.3687178960758] initializing AR(1) noise paramter using MM-algorithm ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_gaussian_ar_cs.jl:37 gcm.β = [1.7792374382746265, 1.4676487199311803, -0.6410042443969289] initializing dispersion using residual sum of squares gcm.τ = [93.3687178960758] initializing AR(1) noise paramter using MM-algorithm This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 6 variables with only lower bounds: 2 variables with lower and upper bounds: 1 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 -2.1237770e+05 0.00e+00 6.64e+02 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 -1.9429064e+05 0.00e+00 1.27e+05 1.7 5.49e+01 - 3.06e-02 1.11e-03h 5 2 -2.1237862e+05 0.00e+00 4.48e+02 1.3 6.01e-02 - 1.00e+00 1.00e+00f 1 3 -2.1238129e+05 0.00e+00 1.17e+03 -0.6 1.61e-03 - 1.00e+00 1.00e+00f 1 4 -2.1238236e+05 0.00e+00 1.01e+03 -2.5 5.83e-03 - 1.00e+00 2.50e-01f 3 5 -2.1238362e+05 0.00e+00 1.11e+02 -1.6 7.01e-04 - 1.00e+00 1.00e+00f 1 6 -2.1238392e+05 0.00e+00 4.13e+02 -2.7 7.88e-04 - 1.00e+00 1.00e+00f 1 7 -2.1238487e+05 0.00e+00 1.00e+03 -3.2 2.65e-02 - 1.00e+00 1.25e-01f 4 8 -2.1239226e+05 0.00e+00 1.30e+03 -4.6 1.56e-02 - 1.00e+00 1.00e+00f 1 9 -2.1243331e+05 0.00e+00 4.04e+03 -3.0 1.60e+00 - 1.00e+00 1.50e-01f 3 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 -2.1244567e+05 0.00e+00 2.81e+03 -3.3 6.07e-02 - 1.00e+00 5.00e-01f 2 11 -2.1245676e+05 0.00e+00 2.04e+02 -4.4 4.09e-02 - 1.00e+00 1.00e+00f 1 12 -2.1245696e+05 0.00e+00 4.30e+01 -6.2 5.39e-03 - 1.00e+00 1.00e+00f 1 13 -2.1245737e+05 0.00e+00 1.62e+02 -8.0 1.20e-02 - 1.00e+00 1.00e+00f 1 14 -2.1245946e+05 0.00e+00 4.63e+02 -9.6 8.47e-02 - 1.00e+00 1.00e+00f 1 15 -2.1246643e+05 0.00e+00 5.39e+02 -9.9 3.31e-01 - 1.00e+00 1.00e+00f 1 16 -2.1247452e+05 0.00e+00 2.25e+03 -9.7 1.75e+00 - 1.00e+00 5.00e-01f 2 17 -2.1249177e+05 0.00e+00 2.23e+03 -9.5 1.20e+01 - 1.00e+00 1.25e-01f 4 18 -2.1249509e+05 0.00e+00 5.90e+02 -9.9 9.48e-01 - 1.00e+00 2.50e-01f 3 19 -2.1249974e+05 0.00e+00 4.37e+02 -11.0 1.24e-01 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 20 -2.1250764e+05 0.00e+00 7.66e+02 -11.0 1.96e-01 - 1.00e+00 1.00e+00f 1 21 -2.1251156e+05 0.00e+00 5.56e+02 -11.0 5.05e-01 - 1.00e+00 1.00e+00f 1 22 -2.1251506e+05 0.00e+00 5.89e+02 -11.0 7.20e-01 - 1.00e+00 1.00e+00f 1 23 -2.1251635e+05 0.00e+00 3.54e+02 -11.0 2.64e-01 - 1.00e+00 1.00e+00f 1 24 -2.1251760e+05 0.00e+00 2.86e+02 -11.0 3.44e-02 - 1.00e+00 1.00e+00f 1 25 -2.1251850e+05 0.00e+00 4.92e+01 -11.0 2.09e-01 - 1.00e+00 1.00e+00f 1 26 -2.1251900e+05 0.00e+00 1.82e+01 -11.0 2.09e-01 - 1.00e+00 1.00e+00f 1 27 -2.1251922e+05 0.00e+00 6.50e+00 -11.0 1.33e-01 - 1.00e+00 1.00e+00f 1 28 -2.1251931e+05 0.00e+00 5.19e+00 -11.0 8.90e-02 - 1.00e+00 1.00e+00f 1 29 -2.1251933e+05 0.00e+00 4.97e-01 -11.0 6.11e-02 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 30 -2.1251934e+05 0.00e+00 5.50e-01 -11.0 2.36e-02 - 1.00e+00 1.00e+00f 1 31 -2.1251934e+05 0.00e+00 2.06e-01 -11.0 9.78e-03 - 1.00e+00 1.00e+00f 1 32 -2.1251934e+05 0.00e+00 2.38e-01 -11.0 8.24e-04 - 1.00e+00 1.00e+00f 1 33 -2.1251934e+05 0.00e+00 3.54e-02 -11.0 3.57e-04 - 1.00e+00 1.00e+00f 1 34 -2.1251934e+05 0.00e+00 5.03e-03 -11.0 4.56e-06 - 1.00e+00 1.00e+00f 1 35 -2.1251934e+05 0.00e+00 2.86e-05 -11.0 4.65e-07 - 1.00e+00 1.00e+00f 1 36 -2.1251934e+05 0.00e+00 2.19e-06 -11.0 2.62e-09 - 1.00e+00 1.00e+00f 1 37 -2.1251934e+05 0.00e+00 1.92e-06 -11.0 8.21e-11 - 1.00e+00 1.25e-01f 4 38 -2.1251934e+05 0.00e+00 9.60e-07 -11.0 7.92e-11 - 1.00e+00 5.00e-01f 2 39 -2.1251934e+05 0.00e+00 7.20e-07 -11.0 3.84e-11 - 1.00e+00 2.50e-01f 3 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 40 -2.1251934e+05 0.00e+00 6.29e-07 -11.0 2.94e-11 - 1.00e+00 1.25e-01f 4 41 -2.1251934e+05 0.00e+00 5.90e-07 -11.0 2.56e-11 - 1.00e+00 6.25e-02f 5 42 -2.1251934e+05 0.00e+00 5.53e-07 -11.0 2.27e-11 - 1.00e+00 6.25e-02h 5 43 -2.1251934e+05 0.00e+00 5.34e-10 -11.0 2.44e-11 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 43 (scaled) (unscaled) Objective...............: -3.8421544444201092e+04 -2.1251933668377565e+05 Dual infeasibility......: 5.3383025263378607e-10 2.9527509326499057e-09 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 9.9999999999999994e-12 5.5312543979697009e-11 Overall NLP error.......: 5.3383025263378607e-10 2.9527509326499057e-09 Number of objective function evaluations = 135 Number of objective gradient evaluations = 44 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.637 Total CPU secs in NLP function evaluations = 6.431 EXIT: Optimal Solution Found. fittime = 7.6063674 gcm.β = [1.779223042971185, 1.4676462559432948, -0.6410063639311059] gcm.σ2 = [0.2856750104155241] gcm.ρ = [0.5419104647586133] gcm.∇β = [2.9527509326499057e-9, 7.362466192262218e-10, 6.29990282163817e-10] gcm.∇σ2 = [-2.290869716148336e-10] gcm.∇ρ = [1.724254072854592e-11] gcm.τ = [99.20408525343595] gcm.∇τ = [-1.337497820830258e-12] QuasiCopula.confint(gcm) = [1.7788743136807275 1.7795717722616426; 1.4673915573760583 1.4679009545105313; -0.6412507926777085 -0.6407619351845033; 0.5059992780140438 0.5778216515031828; 0.19794305733471518 0.37340696349633307; 99.03715009233993 99.37102041453197] mseβ = 1.0267921066863474e-7 mseτ = 6.436858893966583e-9 mseσ2 = 0.04593520116038569 mseρ = 0.0017564870562829643 WARNING: Method definition get_V(Any, Any) in module PkgTest at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_normalAR.jl:19 overwritten at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_poissonAR.jl:18. ┌ Warning: Assignment to `vecd` in soft scope is ambiguous because a global variable by the same name exists: `vecd` will be treated as a new local. Disambiguate by using `local vecd` to suppress this warning or `global vecd` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_poissonAR.jl:55 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_poissonAR.jl:61 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_poissonAR.jl:66 precompiling Poisson AR fit initializing β using Newton's Algorithm under Independence Assumption initializing variance components using MM-Algorithm ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_glm_ar_cs.jl:39 initializing β using Newton's Algorithm under Independence Assumption initializing variance components using MM-Algorithm This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 5 variables with only lower bounds: 1 variables with lower and upper bounds: 1 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 5.7680145e+05 0.00e+00 9.47e+01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 5.7987892e+05 0.00e+00 3.13e+03 1.5 3.43e+01 - 1.64e-02 2.82e-03f 5 2 5.7913283e+05 0.00e+00 8.22e+02 -0.3 2.52e-01 - 1.00e+00 5.00e-01f 2 3 5.7874797e+05 0.00e+00 1.94e+03 -0.3 2.28e-01 - 1.00e+00 5.00e-01f 2 4 5.7703473e+05 0.00e+00 8.06e+02 -0.3 4.45e-02 - 1.00e+00 1.00e+00f 1 5 5.7666820e+05 0.00e+00 4.07e+02 -0.3 3.21e-02 - 1.00e+00 5.00e-01f 2 6 5.7647437e+05 0.00e+00 2.15e+01 -1.9 1.21e-02 - 1.00e+00 1.00e+00f 1 7 5.7647220e+05 0.00e+00 1.88e+01 -2.9 3.30e-03 - 1.00e+00 1.00e+00f 1 8 5.7641260e+05 0.00e+00 4.11e+01 -3.3 1.67e-01 - 1.00e+00 1.00e+00f 1 9 5.7639589e+05 0.00e+00 1.12e+02 -4.1 3.35e-01 - 1.00e+00 2.50e-01f 3 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 5.7638902e+05 0.00e+00 3.79e+01 -4.3 5.83e-01 - 1.00e+00 1.14e-01f 4 11 5.7638482e+05 0.00e+00 3.93e+01 -5.5 2.12e-02 - 1.00e+00 1.00e+00f 1 12 5.7638390e+05 0.00e+00 4.11e+00 -6.9 1.19e-02 - 1.00e+00 1.00e+00f 1 13 5.7638382e+05 0.00e+00 1.74e+00 -8.4 4.81e-03 - 1.00e+00 1.00e+00f 1 14 5.7638369e+05 0.00e+00 3.65e+00 -10.1 9.85e-03 - 1.00e+00 1.00e+00f 1 15 5.7638357e+05 0.00e+00 1.75e+01 -11.0 7.05e-02 - 1.00e+00 1.00e+00f 1 16 5.7638318e+05 0.00e+00 1.42e+01 -11.0 8.22e-02 - 1.00e+00 1.00e+00f 1 17 5.7638295e+05 0.00e+00 3.32e+00 -11.0 1.65e-02 - 1.00e+00 1.00e+00f 1 18 5.7638294e+05 0.00e+00 6.97e-01 -11.0 5.80e-03 - 1.00e+00 1.00e+00f 1 19 5.7638294e+05 0.00e+00 2.11e-01 -11.0 6.07e-03 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 20 5.7638294e+05 0.00e+00 1.11e-01 -11.0 3.65e-03 - 1.00e+00 1.00e+00f 1 21 5.7638294e+05 0.00e+00 9.07e-02 -11.0 9.83e-04 - 1.00e+00 5.00e-01f 2 22 5.7638294e+05 0.00e+00 2.69e-03 -11.0 2.78e-04 - 1.00e+00 1.00e+00f 1 23 5.7638294e+05 0.00e+00 1.95e-04 -11.0 1.81e-05 - 1.00e+00 1.00e+00f 1 24 5.7638294e+05 0.00e+00 1.80e-05 -11.0 1.41e-06 - 1.00e+00 1.00e+00f 1 25 5.7638294e+05 0.00e+00 1.67e-05 -11.0 1.93e-08 - 1.00e+00 6.25e-02h 5 26 5.7638294e+05 0.00e+00 9.57e-09 -11.0 1.81e-08 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 26 (scaled) (unscaled) Objective...............: 6.6495527635699154e+03 5.7638293759266404e+05 Dual infeasibility......: 9.5745372537588562e-09 8.2992046301910705e-07 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 9.9999999999999994e-12 8.6679955492709533e-10 Overall NLP error.......: 9.5745372537588562e-09 8.2992046301910705e-07 Number of objective function evaluations = 76 Number of objective gradient evaluations = 27 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.341 Total CPU secs in NLP function evaluations = 7.934 EXIT: Optimal Solution Found. fittime = 8.603834502 gcm.β = [1.7788291790046011, 1.4675008257387583, -0.6410410738540551] gcm.σ2 = [0.5774215948965639] gcm.ρ = [0.5069090492531296] gcm.∇β = [8.29920463019107e-7, 8.129704553994088e-7, -1.3556282851823198e-7] gcm.∇σ2 = [-3.651297930673536e-9] gcm.∇ρ = [1.08850848334896e-8] QuasiCopula.confint(gcm) = [1.7784982761753616 1.7791600818338407; 1.466700476214771 1.4683011752627455; -0.6418660831183591 -0.640216064589751; 0.4704752781444908 0.5433428203617684; 0.2992818735667331 0.8555613162263946] mseβ = 1.26740393625111e-7 mseσ2 = 0.005994103356327643 mseρ = 4.773496158217108e-5 checking memory allocation for Poisson AR Test Summary: | Pass Total Time AR Covariance | 32 32 3m20.8s WARNING: Method definition get_V(Any, Any) in module PkgTest at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/AR/singlerun_poissonAR.jl:18 overwritten at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_nbCS.jl:17. ┌ Warning: Assignment to `p` in soft scope is ambiguous because a global variable by the same name exists: `p` will be treated as a new local. Disambiguate by using `local p` to suppress this warning or `global p` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_nbCS.jl:57 ┌ Warning: Assignment to `vecd` in soft scope is ambiguous because a global variable by the same name exists: `vecd` will be treated as a new local. Disambiguate by using `local vecd` to suppress this warning or `global vecd` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_nbCS.jl:58 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_nbCS.jl:62 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_nbCS.jl:66 precompiling NB CS fit Initializing NegBin r to Poisson regression values initializing β using Newton's Algorithm under Independence Assumption initializing r using Newton update Converging when tol ≤ 1.0e-6 (max block iter = 1) Block iter 1 r = 9.89, logl = -658739.5, tol = 658739.5039261429 Initializing NegBin r to Poisson regression values initializing β using Newton's Algorithm under Independence Assumption initializing r using Newton update Converging when tol ≤ 1.0e-6 (max block iter = 10) Block iter 1 r = 9.89, logl = -658739.5, tol = 658739.5039261429 Block iter 2 r = 10.01, logl = -658714.24, tol = 3.835161499281299e-5 Block iter 3 r = 10.03, logl = -658710.71, tol = 5.364935332415035e-6 Block iter 4 r = 10.04, logl = -658710.64, tol = 1.0584168328656227e-7 fittime = 47.873444728 gcm.β = [1.779890858540379, 1.4666263470733032, -0.6421677298926708] gcm.σ2 = [0.6797455449258283] gcm.ρ = [0.48096396209003445] gcm.∇β = [-322.09678820407754, 281.3670088675276, 366.2738251995943] gcm.∇σ2 = [2.5822535515301004] gcm.∇ρ = [95.10503905225801] gcm.r = [10.037011792917179] gcm.∇r = [-411.2575507695234] QuasiCopula.confint(gcm) = [1.7779129805800111 1.7818687365007468; 1.4650948022516619 1.4681578918949445; -0.643233321441434 -0.6411021383439076; 0.4607341735985528 0.501193750581516; 0.47177948591039565 0.8877116039412609; 9.919431425923616 10.154592159910742] mseβ = 6.053740874078165e-7 mser = 0.00136987281494412 mseσ2 = 0.03230846092068295 mseρ = 0.0003623707393096457 WARNING: Method definition get_V(Any, Any) in module PkgTest at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_nbCS.jl:17 overwritten at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_bernoulliCS.jl:17. ┌ Warning: Assignment to `vecd` in soft scope is ambiguous because a global variable by the same name exists: `vecd` will be treated as a new local. Disambiguate by using `local vecd` to suppress this warning or `global vecd` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_bernoulliCS.jl:56 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_bernoulliCS.jl:62 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_bernoulliCS.jl:67 precompiling Bernoulli CS fit initializing β using Newton's Algorithm under Independence Assumption ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_glm_ar_cs.jl:85 initializing β using Newton's Algorithm under Independence Assumption This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Starting derivative checker for first derivatives. * grad_f[ 5] = -2.0412931107051357e+01 ~ -2.0298436468921924e+01 [ 5.641e-03] Derivative checker detected 1 error(s). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 5 variables with only lower bounds: 1 variables with lower and upper bounds: 1 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 1.1095612e+05 0.00e+00 1.00e+02 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 1.0989619e+05 0.00e+00 4.72e+01 -4.1 9.95e+01 - 2.40e-03 3.98e-03f 2 2 1.0981989e+05 0.00e+00 3.13e+01 -6.1 1.17e-01 - 8.14e-01 5.00e-01f 2 3 1.0977366e+05 0.00e+00 3.87e+00 -7.2 6.58e-02 - 8.97e-01 1.00e+00f 1 4 1.0977235e+05 0.00e+00 8.56e-01 -9.7 1.51e-02 - 9.64e-01 1.00e+00f 1 5 1.0977233e+05 0.00e+00 1.33e+00 -11.0 2.76e-03 - 9.89e-01 1.00e+00f 1 6 1.0977227e+05 0.00e+00 6.14e-01 -11.0 1.19e-03 - 9.92e-01 1.00e+00f 1 7 1.0977218e+05 0.00e+00 6.22e-01 -11.0 5.23e-03 - 1.00e+00 1.00e+00f 1 8 1.0977161e+05 0.00e+00 2.75e+00 -11.0 4.67e-02 - 1.00e+00 1.00e+00f 1 9 1.0977059e+05 0.00e+00 9.64e+00 -11.0 1.99e+00 - 1.00e+00 1.16e-01f 3 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 1.0976783e+05 0.00e+00 1.23e+01 -11.0 7.14e-01 - 1.00e+00 2.43e-01f 3 11 1.0976736e+05 0.00e+00 1.13e+01 -11.0 1.41e+00 - 1.00e+00 9.27e-02f 3 12 1.0976368e+05 0.00e+00 3.45e+00 -11.0 1.08e-01 - 8.98e-01 1.00e+00f 1 13 1.0976320e+05 0.00e+00 4.18e+00 -11.0 3.77e-02 - 1.00e+00 1.00e+00f 1 14 1.0976264e+05 0.00e+00 4.24e-01 -11.0 1.68e-02 - 1.00e+00 1.00e+00f 1 15 1.0976262e+05 0.00e+00 2.35e-01 -11.0 8.89e-03 - 1.00e+00 1.00e+00f 1 16 1.0976261e+05 0.00e+00 7.53e-02 -11.0 1.35e-03 - 1.00e+00 1.00e+00f 1 17 1.0976261e+05 0.00e+00 5.05e-03 -11.0 1.47e-04 - 1.00e+00 1.00e+00f 1 18 1.0976261e+05 0.00e+00 2.11e-04 -11.0 4.84e-05 - 1.00e+00 1.00e+00f 1 19 1.0976261e+05 0.00e+00 1.01e-05 -11.0 1.65e-06 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 20 1.0976261e+05 0.00e+00 4.90e-06 -11.0 1.71e-07 - 1.00e+00 5.00e-01f 2 21 1.0976261e+05 0.00e+00 1.13e-08 -11.0 7.29e-08 - 1.00e+00 1.00e+00f 1 22 1.0976261e+05 0.00e+00 8.02e-09 -11.0 6.31e-11 - 1.00e+00 2.50e-01h 3 Number of Iterations....: 22 (scaled) (unscaled) Objective...............: 3.7653834361600220e+03 1.0976261096253175e+05 Dual infeasibility......: 8.0239166180916141e-09 2.3390075754026185e-07 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 1.0000000001075827e-11 2.9150447181622856e-10 Overall NLP error.......: 8.0239166180916141e-09 2.3390075754026185e-07 Number of objective function evaluations = 62 Number of objective gradient evaluations = 23 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.595 Total CPU secs in NLP function evaluations = 4.789 EXIT: Optimal Solution Found. fittime = 5.64700131 gcm.β = [1.7913575161296567, 1.476827599973903, -0.6585979050608902] gcm.σ2 = [0.4397282082930054] gcm.ρ = [0.5226503967057576] gcm.∇β = [1.1454351855899603e-7, -2.3390075754026185e-7, 7.138621316293836e-8] gcm.∇σ2 = [-1.5203099668070763e-8] gcm.∇ρ = [-1.1902942165242791e-7] get_CI(gcm) = [1.778866037824406 1.8038489944349072; 1.4694826343381684 1.4841725656096374; -0.6679528033750266 -0.6492430067467538; 0.5007995985955427 0.5445011948159726; 0.3553040858257656 0.5241523307602451] mseβ = 0.00017491529086561934 mseσ2 = 0.0036326888755713422 mseρ = 0.0005130404709281964 checking memory allocation for bernoulli CS WARNING: Method definition get_V(Any, Any) in module PkgTest at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_bernoulliCS.jl:17 overwritten at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_normalCS.jl:19. ┌ Warning: Assignment to `vecd` in soft scope is ambiguous because a global variable by the same name exists: `vecd` will be treated as a new local. Disambiguate by using `local vecd` to suppress this warning or `global vecd` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_normalCS.jl:48 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_normalCS.jl:54 precompiling Gaussian CS fit gcm.β = [1.779276929891613, 1.4676549108846602, -0.6409977146702235] initializing dispersion using residual sum of squares gcm.τ = [93.34351737067983] initializing CS noise paramter using MM-algorithm ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_gaussian_ar_cs.jl:37 gcm.β = [1.779276929891613, 1.4676549108846602, -0.6409977146702235] initializing dispersion using residual sum of squares gcm.τ = [93.34351737067983] initializing CS noise paramter using MM-algorithm This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 6 variables with only lower bounds: 2 variables with lower and upper bounds: 1 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 -2.1365061e+05 0.00e+00 9.84e+01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 9.4423192e+05 0.00e+00 8.07e+05 1.4 9.84e+01 - 2.73e-01 2.58e-03h 5 2 -2.1365064e+05 0.00e+00 2.56e+02 1.3 2.54e-01 - 1.00e+00 1.00e+00f 1 3 -2.1365065e+05 0.00e+00 2.95e+01 -0.6 1.24e-05 - 1.00e+00 1.00e+00f 1 4 -2.1365065e+05 0.00e+00 2.48e+01 -2.5 2.76e-05 - 1.00e+00 1.00e+00f 1 5 -2.1365068e+05 0.00e+00 6.44e+01 -4.2 2.02e-04 - 1.00e+00 1.00e+00f 1 6 -2.1365069e+05 0.00e+00 1.47e+02 -5.6 1.13e-03 - 1.00e+00 2.50e-01f 3 7 -2.1365071e+05 0.00e+00 1.11e+02 -7.4 6.01e-04 - 1.00e+00 5.00e-01f 2 8 -2.1365075e+05 0.00e+00 7.63e+01 -8.6 3.56e-03 - 1.00e+00 1.25e-01f 4 9 -2.1365076e+05 0.00e+00 1.85e+01 -10.5 1.43e-04 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 -2.1365076e+05 0.00e+00 5.49e+00 -11.0 7.13e-05 - 1.00e+00 1.00e+00f 1 11 -2.1365077e+05 0.00e+00 3.99e+01 -11.0 4.05e-04 - 1.00e+00 1.00e+00f 1 12 -2.1365086e+05 0.00e+00 1.11e+02 -11.0 3.55e-03 - 1.00e+00 1.00e+00f 1 13 -2.1365098e+05 0.00e+00 3.64e+02 -10.3 6.95e-02 - 1.00e+00 1.25e-01f 4 14 -2.1365150e+05 0.00e+00 5.71e+02 -10.7 3.65e-02 - 1.00e+00 1.00e+00f 1 15 -2.1365210e+05 0.00e+00 5.63e+02 -11.0 2.89e-01 - 1.00e+00 1.25e-01f 4 16 -2.1365302e+05 0.00e+00 1.87e+02 -11.0 1.60e-02 - 1.00e+00 1.00e+00f 1 17 -2.1365362e+05 0.00e+00 6.22e+01 -11.0 2.88e-02 - 1.00e+00 1.00e+00f 1 18 -2.1365380e+05 0.00e+00 2.01e+02 -11.0 1.53e-02 - 1.00e+00 1.00e+00f 1 19 -2.1365411e+05 0.00e+00 1.50e+02 -11.0 3.32e-02 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 20 -2.1365444e+05 0.00e+00 6.61e+01 -11.0 4.95e-02 - 1.00e+00 1.00e+00f 1 21 -2.1365480e+05 0.00e+00 4.39e+02 -11.0 7.43e-01 - 1.00e+00 1.00e+00f 1 22 -2.1365908e+05 0.00e+00 5.26e+02 -11.0 8.55e-02 - 1.00e+00 1.00e+00f 1 23 -2.1366082e+05 0.00e+00 2.81e+02 -11.0 2.78e-01 - 1.00e+00 1.00e+00f 1 24 -2.1366110e+05 0.00e+00 1.88e+02 -11.0 1.30e+00 - 1.00e+00 5.00e-01f 2 25 -2.1366219e+05 0.00e+00 4.87e+01 -11.0 2.27e-01 - 1.00e+00 1.00e+00f 1 26 -2.1366245e+05 0.00e+00 4.19e+01 -11.0 1.20e-01 - 1.00e+00 1.00e+00f 1 27 -2.1366280e+05 0.00e+00 3.38e+01 -11.0 2.25e-01 - 1.00e+00 1.00e+00f 1 28 -2.1366311e+05 0.00e+00 7.93e+01 -11.0 1.82e-02 - 1.00e+00 1.00e+00f 1 29 -2.1366376e+05 0.00e+00 5.40e+01 -11.0 9.71e-02 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 30 -2.1366404e+05 0.00e+00 6.34e+00 -11.0 1.49e-01 - 1.00e+00 1.00e+00f 1 31 -2.1366426e+05 0.00e+00 7.29e+01 -11.0 8.66e-02 - 1.00e+00 1.00e+00f 1 32 -2.1366433e+05 0.00e+00 2.49e+01 -11.0 1.78e-01 - 1.00e+00 1.00e+00f 1 33 -2.1366438e+05 0.00e+00 1.61e+01 -11.0 3.95e-02 - 1.00e+00 1.00e+00f 1 34 -2.1366439e+05 0.00e+00 1.31e+01 -11.0 3.70e-02 - 1.00e+00 1.00e+00f 1 35 -2.1366440e+05 0.00e+00 1.12e+00 -11.0 5.07e-02 - 1.00e+00 1.00e+00f 1 36 -2.1366440e+05 0.00e+00 6.74e-01 -11.0 7.35e-03 - 1.00e+00 1.00e+00f 1 37 -2.1366440e+05 0.00e+00 1.16e-01 -11.0 3.34e-03 - 1.00e+00 1.00e+00f 1 38 -2.1366440e+05 0.00e+00 7.62e-02 -11.0 9.79e-05 - 1.00e+00 1.00e+00f 1 39 -2.1366440e+05 0.00e+00 2.44e-03 -11.0 1.02e-05 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 40 -2.1366440e+05 0.00e+00 3.06e-04 -11.0 1.47e-06 - 1.00e+00 1.00e+00f 1 41 -2.1366440e+05 0.00e+00 1.07e-05 -11.0 3.15e-07 - 1.00e+00 1.00e+00f 1 42 -2.1366440e+05 0.00e+00 7.51e-07 -11.0 9.77e-10 - 1.00e+00 1.00e+00f 1 43 -2.1366440e+05 0.00e+00 3.51e-07 -11.0 3.16e-10 - 1.00e+00 5.00e-01f 2 44 -2.1366440e+05 0.00e+00 3.29e-07 -11.0 1.35e-10 - 1.00e+00 6.25e-02f 5 45 -2.1366440e+05 0.00e+00 2.88e-07 -11.0 1.28e-10 - 1.00e+00 1.25e-01f 4 46 -2.1366440e+05 0.00e+00 2.22e-10 -11.0 1.09e-10 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 46 (scaled) (unscaled) Objective...............: -2.8441400209500098e+04 -2.1366440136252291e+05 Dual infeasibility......: 2.2175911114799513e-10 1.6659527091178461e-09 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 9.9999999999999994e-12 7.5124431212480870e-11 Overall NLP error.......: 2.2175911114799513e-10 1.6659527091178461e-09 Number of objective function evaluations = 112 Number of objective gradient evaluations = 47 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.667 Total CPU secs in NLP function evaluations = 5.204 EXIT: Optimal Solution Found. fittime = 6.288449173 gcm.β = [1.7792948465291916, 1.4676653561289281, -0.6410233297658633] gcm.σ2 = [0.276613337660851] gcm.ρ = [0.5668229616764449] gcm.∇β = [-1.4002008441593716e-9, 1.665952709117846e-9, 1.8070878127218748e-11] gcm.∇σ2 = [-3.0215341340067425e-10] gcm.∇ρ = [4.6869397252180534e-11] gcm.τ = [99.13578908419973] gcm.∇τ = [8.268958434642926e-13] QuasiCopula.confint(gcm) = [1.779090365813433 1.7794993272449502; 1.467386158206457 1.4679445540513993; -0.6412919452937041 -0.6407547142380224; 0.543663143714226 0.5899827796386637; 0.23061728097319995 0.32260939434850205; 98.97677219846248 99.29480596993697] mseβ = 1.0218049140922073e-7 mseτ = 7.599386965055088e-9 mseσ2 = 0.04990160091102497 mseρ = 0.004465308207211618 WARNING: Method definition get_V(Any, Any) in module PkgTest at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_normalCS.jl:19 overwritten at /home/pkgeval/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_poissonCS.jl:17. ┌ Warning: Assignment to `vecd` in soft scope is ambiguous because a global variable by the same name exists: `vecd` will be treated as a new local. Disambiguate by using `local vecd` to suppress this warning or `global vecd` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_poissonCS.jl:54 ┌ Warning: Assignment to `y` in soft scope is ambiguous because a global variable by the same name exists: `y` will be treated as a new local. Disambiguate by using `local y` to suppress this warning or `global y` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_poissonCS.jl:60 ┌ Warning: Assignment to `V` in soft scope is ambiguous because a global variable by the same name exists: `V` will be treated as a new local. Disambiguate by using `local V` to suppress this warning or `global V` to assign to the existing global variable. └ @ ~/.julia/packages/QuasiCopula/4PoKI/test/unit_test/CS/singlerun_poissonCS.jl:64 precompiling Poisson CS fit initializing β using Newton's Algorithm under Independence Assumption ┌ Warning: Optimization unsuccesful; got UserLimit └ @ QuasiCopula ~/.julia/packages/QuasiCopula/4PoKI/src/parameter_estimation/fit_glm_ar_cs.jl:85 initializing β using Newton's Algorithm under Independence Assumption This is Ipopt version 3.13.4, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 0 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 0 Total number of variables............................: 5 variables with only lower bounds: 1 variables with lower and upper bounds: 1 variables with only upper bounds: 0 Total number of equality constraints.................: 0 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 5.7618487e+05 0.00e+00 1.00e+02 0.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 1.6056291e+06 0.00e+00 6.01e+04 1.2 1.00e+02 - 1.00e+00 1.21e-02h 3 2 5.7744833e+05 0.00e+00 3.17e+03 1.0 1.18e+00 - 1.00e+00 1.00e+00f 1 3 5.9568629e+05 0.00e+00 1.24e+04 1.0 4.78e-01 - 1.00e+00 2.50e-01h 3 4 5.7615447e+05 0.00e+00 1.92e+02 1.0 9.77e-02 - 1.00e+00 1.00e+00f 1 5 5.7614654e+05 0.00e+00 5.55e+01 -0.9 1.57e-03 - 1.00e+00 1.00e+00f 1 6 5.7614330e+05 0.00e+00 5.49e+01 -2.8 1.11e-03 - 1.00e+00 1.00e+00f 1 7 5.7611836e+05 0.00e+00 2.22e+02 -4.2 1.25e-02 - 1.00e+00 1.00e+00f 1 8 5.7645652e+05 0.00e+00 1.82e+03 -4.7 3.84e-01 - 1.00e+00 2.50e-01h 3 9 5.7597655e+05 0.00e+00 5.16e+02 -4.7 2.36e-02 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10 5.7592216e+05 0.00e+00 2.91e+02 -5.9 1.95e-02 - 1.00e+00 1.00e+00f 1 11 5.7578885e+05 0.00e+00 1.10e+02 -7.1 1.09e-01 - 1.00e+00 1.00e+00f 1 12 5.7576852e+05 0.00e+00 1.46e+01 -9.0 5.41e-02 - 1.00e+00 1.00e+00f 1 13 5.7576848e+05 0.00e+00 1.11e+00 -9.7 1.36e-03 - 1.00e+00 1.00e+00f 1 14 5.7576848e+05 0.00e+00 4.52e-01 -11.0 1.29e-04 - 1.00e+00 1.00e+00f 1 15 5.7576847e+05 0.00e+00 3.37e+00 -11.0 2.32e-03 - 1.00e+00 1.00e+00f 1 16 5.7576839e+05 0.00e+00 1.26e+01 -11.0 1.65e-02 - 1.00e+00 1.00e+00f 1 17 5.7576823e+05 0.00e+00 2.28e+01 -11.0 2.57e-01 - 1.00e+00 2.50e-01f 3 18 5.7576709e+05 0.00e+00 7.08e+01 -11.0 6.91e-01 - 1.00e+00 5.00e-01f 2 19 5.7576869e+05 0.00e+00 1.35e+02 -11.0 2.18e+00 - 1.00e+00 6.58e-02h 3 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 20 5.7576666e+05 0.00e+00 8.37e+01 -10.8 1.68e-01 - 1.00e+00 1.00e+00f 1 21 5.7576655e+05 0.00e+00 4.19e+01 -11.0 1.45e-01 - 1.00e+00 5.00e-01f 2 22 5.7576558e+05 0.00e+00 1.59e+01 -11.0 8.36e-03 - 1.00e+00 1.00e+00f 1 23 5.7576543e+05 0.00e+00 8.62e+00 -11.0 3.62e-02 - 1.00e+00 1.00e+00f 1 24 5.7576540e+05 0.00e+00 2.29e+00 -11.0 2.36e-02 - 1.00e+00 1.00e+00f 1 25 5.7576540e+05 0.00e+00 3.98e-01 -11.0 3.48e-03 - 1.00e+00 1.00e+00f 1 26 5.7576540e+05 0.00e+00 2.18e-02 -11.0 1.13e-03 - 1.00e+00 1.00e+00f 1 27 5.7576540e+05 0.00e+00 5.55e-03 -11.0 2.20e-05 - 1.00e+00 1.00e+00f 1 28 5.7576540e+05 0.00e+00 2.64e-03 -11.0 2.72e-06 - 1.00e+00 1.00e+00f 1 29 5.7576540e+05 0.00e+00 2.91e-05 -11.0 3.79e-06 - 1.00e+00 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 30 5.7576540e+05 0.00e+00 9.38e-07 -11.0 5.53e-08 - 1.00e+00 1.00e+00f 1 31 5.7576540e+05 0.00e+00 4.34e-09 -11.0 1.47e-09 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 31 (scaled) (unscaled) Objective...............: 1.0814792887896319e+04 5.7576539931003621e+05 Dual infeasibility......: 4.3386418124199638e-09 2.3098360379947280e-07 Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00 Complementarity.........: 1.0000000000000001e-11 5.3238689383910472e-10 Overall NLP error.......: 4.3386418124199638e-09 2.3098360379947280e-07 Number of objective function evaluations = 72 Number of objective gradient evaluations = 32 Number of equality constraint evaluations = 0 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 0 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 0 Total CPU secs in IPOPT (w/o function evaluations) = 0.383 Total CPU secs in NLP function evaluations = 6.742 EXIT: Optimal Solution Found. fittime = 7.36648705 gcm.β = [1.7789748243929642, 1.467540885791087, -0.6412350214953239] gcm.σ2 = [0.4655663905264826] gcm.ρ = [0.5171394407609295] gcm.∇β = [-5.90183368842645e-9, 2.309836037994728e-7, -1.13420826508559e-7] gcm.∇σ2 = [-1.4812777671124877e-9] gcm.∇ρ = [3.1084778084888853e-9] QuasiCopula.confint(gcm) = [1.7783402188092645 1.7796094299766638; 1.4668035679432747 1.4682782036388993; -0.6418509317035805 -0.6406191112870672; 0.495985570667494 0.5382933108543652; 0.3521114899533524 0.5790212910996128] mseβ = 4.612221053221268e-8 mseσ2 = 0.0011856734613747088 mseρ = 0.0002937604295974132 checking memory allocation for Poisson CS Test Summary: | Pass Total Time CS Covariance | 32 32 4m00.4s Testing QuasiCopula tests passed Testing completed after 935.96s PkgEval succeeded after 1080.43s