Package evaluation of Mamba on Julia 1.10.9 (96dc2d8c45*) started at 2025-06-06T21:22:34.309 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 5.2s ################################################################################ # Installation # Installing Mamba... Resolving package versions... Updating `~/.julia/environments/v1.10/Project.toml` [5424a776] + Mamba v0.12.5 Updating `~/.julia/environments/v1.10/Manifest.toml` [621f4979] + AbstractFFTs v1.5.0 [79e6a3ab] + Adapt v4.3.0 ⌅ [ec485272] + ArnoldiMethod v0.1.0 ⌅ [7d9fca2a] + Arpack v0.4.0 [13072b0f] + AxisAlgorithms v1.1.0 [159f3aea] + Cairo v1.1.1 [49dc2e85] + Calculus v0.5.2 [324d7699] + CategoricalArrays v0.10.8 [d360d2e6] + ChainRulesCore v1.25.1 ⌅ [3da002f7] + ColorTypes v0.11.5 ⌅ [5ae59095] + Colors v0.12.11 [34da2185] + Compat v4.16.0 [a81c6b42] + Compose v0.9.6 [d38c429a] + Contour v0.6.3 ⌅ [7ad07ef1] + CoupledFields v0.2.0 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.22 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 ⌅ [31c24e10] + Distributions v0.23.4 [ffbed154] + DocStringExtensions v0.9.4 [7a1cc6ca] + FFTW v1.9.0 ⌅ [1a297f60] + FillArrays v0.8.14 [53c48c17] + FixedPointNumbers v0.8.5 [c91e804a] + Gadfly v1.4.0 [a2bd30eb] + Graphics v1.1.3 [42e2da0e] + Grisu v1.0.2 [a1b4810d] + Hexagons v0.2.0 [9b13fd28] + IndirectArrays v1.0.0 [d25df0c9] + Inflate v0.1.5 ⌅ [a98d9a8b] + Interpolations v0.15.1 [92d709cd] + IrrationalConstants v0.2.4 [c8e1da08] + IterTools v1.10.0 [692b3bcd] + JLLWrappers v1.7.0 [682c06a0] + JSON v0.21.4 [e5e0dc1b] + Juno v0.8.4 [5ab0869b] + KernelDensity v0.6.9 [093fc24a] + LightGraphs v1.3.5 [4345ca2d] + Loess v0.6.4 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.16 [5424a776] + Mamba v0.12.5 [442fdcdd] + Measures v0.3.2 [e89f7d12] + Media v0.5.0 [e1d29d7a] + Missings v1.2.0 [77ba4419] + NaNMath v1.1.3 [6fe1bfb0] + OffsetArrays v1.17.0 [bac558e1] + OrderedCollections v1.8.1 ⌅ [90014a1f] + PDMats v0.9.12 [69de0a69] + Parsers v2.8.3 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [1fd47b50] + QuadGK v2.11.2 [c84ed2f1] + Ratios v0.4.5 ⌅ [189a3867] + Reexport v0.2.0 [ae029012] + Requires v1.3.1 ⌅ [79098fc4] + Rmath v0.7.1 ⌅ [992d4aef] + Showoff v0.3.2 [699a6c99] + SimpleTraits v0.9.4 [a2af1166] + SortingAlgorithms v1.2.1 ⌅ [276daf66] + SpecialFunctions v0.10.3 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [82ae8749] + StatsAPI v1.7.1 ⌅ [2913bbd2] + StatsBase v0.33.21 ⌅ [4c63d2b9] + StatsFuns v0.9.7 [efce3f68] + WoodburyMatrices v1.0.0 ⌅ [68821587] + Arpack_jll v3.5.1+1 [6e34b625] + Bzip2_jll v1.0.9+0 [83423d85] + Cairo_jll v1.18.5+0 [2e619515] + Expat_jll v2.6.5+0 [f5851436] + FFTW_jll v3.3.11+0 [a3f928ae] + Fontconfig_jll v2.16.0+0 [d7e528f0] + FreeType2_jll v2.13.4+0 [559328eb] + FriBidi_jll v1.0.17+0 [78b55507] + Gettext_jll v0.21.0+0 [7746bdde] + Glib_jll v2.84.0+0 [3b182d85] + Graphite2_jll v1.3.15+0 [2e76f6c2] + HarfBuzz_jll v8.5.1+0 [1d5cc7b8] + IntelOpenMP_jll v2025.0.4+0 [1d63c593] + LLVMOpenMP_jll v18.1.8+0 [dd4b983a] + LZO_jll v2.10.3+0 [e9f186c6] + Libffi_jll v3.4.7+0 [94ce4f54] + Libiconv_jll v1.18.0+0 [4b2f31a3] + Libmount_jll v2.41.0+0 [38a345b3] + Libuuid_jll v2.41.0+0 [856f044c] + MKL_jll v2025.0.1+1 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [36c8627f] + Pango_jll v1.56.3+0 ⌅ [30392449] + Pixman_jll v0.44.2+0 ⌅ [f50d1b31] + Rmath_jll v0.4.3+0 ⌅ [02c8fc9c] + XML2_jll v2.13.6+1 [4f6342f7] + Xorg_libX11_jll v1.8.12+0 [0c0b7dd1] + Xorg_libXau_jll v1.0.13+0 [a3789734] + Xorg_libXdmcp_jll v1.1.6+0 [1082639a] + Xorg_libXext_jll v1.3.7+0 [ea2f1a96] + Xorg_libXrender_jll v0.9.12+0 [c7cfdc94] + Xorg_libxcb_jll v1.17.1+0 [c5fb5394] + Xorg_xtrans_jll v1.6.0+0 [b53b4c65] + libpng_jll v1.6.48+0 [1317d2d5] + oneTBB_jll v2022.0.0+0 [0dad84c5] + ArgTools v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [ade2ca70] + Dates [8ba89e20] + Distributed [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 [1a1011a3] + SharedArrays [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 v0.8.5+0 [efcefdf7] + PCRE2_jll v10.42.0+1 [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 ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 10.21s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 88.99s ################################################################################ # Testing # Testing Mamba Status `/tmp/jl_lHeUil/Project.toml` [159f3aea] Cairo v1.1.1 [49dc2e85] Calculus v0.5.2 [a81c6b42] Compose v0.9.6 [8bb1440f] DelimitedFiles v1.9.1 ⌅ [31c24e10] Distributions v0.23.4 [c91e804a] Gadfly v1.4.0 [093fc24a] LightGraphs v1.3.5 [5424a776] Mamba v0.12.5 ⌅ [90014a1f] PDMats v0.9.12 ⌅ [189a3867] Reexport v0.2.0 ⌅ [992d4aef] Showoff v0.3.2 ⌅ [276daf66] SpecialFunctions v0.10.3 ⌅ [2913bbd2] StatsBase v0.33.21 [8ba89e20] Distributed [37e2e46d] LinearAlgebra [de0858da] Printf [9a3f8284] Random [9e88b42a] Serialization [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [8dfed614] Test Status `/tmp/jl_lHeUil/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [79e6a3ab] Adapt v4.3.0 ⌅ [ec485272] ArnoldiMethod v0.1.0 ⌅ [7d9fca2a] Arpack v0.4.0 [13072b0f] AxisAlgorithms v1.1.0 [159f3aea] Cairo v1.1.1 [49dc2e85] Calculus v0.5.2 [324d7699] CategoricalArrays v0.10.8 [d360d2e6] ChainRulesCore v1.25.1 ⌅ [3da002f7] ColorTypes v0.11.5 ⌅ [5ae59095] Colors v0.12.11 [34da2185] Compat v4.16.0 [a81c6b42] Compose v0.9.6 [d38c429a] Contour v0.6.3 ⌅ [7ad07ef1] CoupledFields v0.2.0 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.18.22 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 ⌅ [31c24e10] Distributions v0.23.4 [ffbed154] DocStringExtensions v0.9.4 [7a1cc6ca] FFTW v1.9.0 ⌅ [1a297f60] FillArrays v0.8.14 [53c48c17] FixedPointNumbers v0.8.5 [c91e804a] Gadfly v1.4.0 [a2bd30eb] Graphics v1.1.3 [42e2da0e] Grisu v1.0.2 [a1b4810d] Hexagons v0.2.0 [9b13fd28] IndirectArrays v1.0.0 [d25df0c9] Inflate v0.1.5 ⌅ [a98d9a8b] Interpolations v0.15.1 [92d709cd] IrrationalConstants v0.2.4 [c8e1da08] IterTools v1.10.0 [692b3bcd] JLLWrappers v1.7.0 [682c06a0] JSON v0.21.4 [e5e0dc1b] Juno v0.8.4 [5ab0869b] KernelDensity v0.6.9 [093fc24a] LightGraphs v1.3.5 [4345ca2d] Loess v0.6.4 [2ab3a3ac] LogExpFunctions v0.3.29 [1914dd2f] MacroTools v0.5.16 [5424a776] Mamba v0.12.5 [442fdcdd] Measures v0.3.2 [e89f7d12] Media v0.5.0 [e1d29d7a] Missings v1.2.0 [77ba4419] NaNMath v1.1.3 [6fe1bfb0] OffsetArrays v1.17.0 [bac558e1] OrderedCollections v1.8.1 ⌅ [90014a1f] PDMats v0.9.12 [69de0a69] Parsers v2.8.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [1fd47b50] QuadGK v2.11.2 [c84ed2f1] Ratios v0.4.5 ⌅ [189a3867] Reexport v0.2.0 [ae029012] Requires v1.3.1 ⌅ [79098fc4] Rmath v0.7.1 ⌅ [992d4aef] Showoff v0.3.2 [699a6c99] SimpleTraits v0.9.4 [a2af1166] SortingAlgorithms v1.2.1 ⌅ [276daf66] SpecialFunctions v0.10.3 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 [82ae8749] StatsAPI v1.7.1 ⌅ [2913bbd2] StatsBase v0.33.21 ⌅ [4c63d2b9] StatsFuns v0.9.7 [efce3f68] WoodburyMatrices v1.0.0 ⌅ [68821587] Arpack_jll v3.5.1+1 [6e34b625] Bzip2_jll v1.0.9+0 [83423d85] Cairo_jll v1.18.5+0 [2e619515] Expat_jll v2.6.5+0 [f5851436] FFTW_jll v3.3.11+0 [a3f928ae] Fontconfig_jll v2.16.0+0 [d7e528f0] FreeType2_jll v2.13.4+0 [559328eb] FriBidi_jll v1.0.17+0 [78b55507] Gettext_jll v0.21.0+0 [7746bdde] Glib_jll v2.84.0+0 [3b182d85] Graphite2_jll v1.3.15+0 [2e76f6c2] HarfBuzz_jll v8.5.1+0 [1d5cc7b8] IntelOpenMP_jll v2025.0.4+0 [1d63c593] LLVMOpenMP_jll v18.1.8+0 [dd4b983a] LZO_jll v2.10.3+0 [e9f186c6] Libffi_jll v3.4.7+0 [94ce4f54] Libiconv_jll v1.18.0+0 [4b2f31a3] Libmount_jll v2.41.0+0 [38a345b3] Libuuid_jll v2.41.0+0 [856f044c] MKL_jll v2025.0.1+1 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [36c8627f] Pango_jll v1.56.3+0 ⌅ [30392449] Pixman_jll v0.44.2+0 ⌅ [f50d1b31] Rmath_jll v0.4.3+0 ⌅ [02c8fc9c] XML2_jll v2.13.6+1 [4f6342f7] Xorg_libX11_jll v1.8.12+0 [0c0b7dd1] Xorg_libXau_jll v1.0.13+0 [a3789734] Xorg_libXdmcp_jll v1.1.6+0 [1082639a] Xorg_libXext_jll v1.3.7+0 [ea2f1a96] Xorg_libXrender_jll v0.9.12+0 [c7cfdc94] Xorg_libxcb_jll v1.17.1+0 [c5fb5394] Xorg_xtrans_jll v1.6.0+0 [b53b4c65] libpng_jll v1.6.48+0 [1317d2d5] oneTBB_jll v2022.0.0+0 [0dad84c5] ArgTools v1.1.1 [56f22d72] Artifacts [2a0f44e3] Base64 [ade2ca70] Dates [8ba89e20] Distributed [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 [1a1011a3] SharedArrays [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 v0.8.5+0 [efcefdf7] PCRE2_jll v10.42.0+1 [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 ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Running tests: >>> Testing ../doc/tutorial/line.jl digraph MambaModel { "beta" [shape="ellipse"]; "beta" -> "mu"; "y" [shape="ellipse", style="filled", fillcolor="gray85"]; "mu" [shape="diamond", style="filled", fillcolor="gray85"]; "mu" -> "y"; "s2" [shape="ellipse"]; "s2" -> "y"; "xmat" [shape="box", style="filled", fillcolor="gray85"]; "xmat" -> "mu"; } MCMC Simulation of 10000 Iterations x 3 Chains... Chain 1: 0% [1:14:02 of 1:14:06 remaining] Chain 1: 10% [0:00:57 of 0:01:04 remaining] Chain 1: 20% [0:00:27 of 0:00:34 remaining] Chain 1: 30% [0:00:16 of 0:00:23 remaining] Chain 1: 40% [0:00:11 of 0:00:18 remaining] Chain 1: 50% [0:00:08 of 0:00:16 remaining] Chain 1: 60% [0:00:05 of 0:00:14 remaining] Chain 1: 70% [0:00:04 of 0:00:12 remaining] Chain 1: 80% [0:00:02 of 0:00:11 remaining] Chain 1: 90% [0:00:01 of 0:00:10 remaining] Chain 1: 100% [0:00:00 of 0:00:10 remaining] Chain 2: 0% [0:00:04 of 0:00:04 remaining] Chain 2: 10% [0:00:02 of 0:00:02 remaining] Chain 2: 20% [0:00:02 of 0:00:02 remaining] Chain 2: 30% [0:00:01 of 0:00:02 remaining] Chain 2: 40% [0:00:01 of 0:00:02 remaining] Chain 2: 50% [0:00:01 of 0:00:02 remaining] Chain 2: 60% [0:00:01 of 0:00:02 remaining] Chain 2: 70% [0:00:01 of 0:00:02 remaining] Chain 2: 80% [0:00:00 of 0:00:02 remaining] Chain 2: 90% [0:00:00 of 0:00:02 remaining] Chain 2: 100% [0:00:00 of 0:00:02 remaining] Chain 3: 0% [0:00:02 of 0:00:02 remaining] Chain 3: 10% [0:00:03 of 0:00:04 remaining] Chain 3: 20% [0:00:03 of 0:00:03 remaining] Chain 3: 30% [0:00:02 of 0:00:03 remaining] Chain 3: 40% [0:00:02 of 0:00:03 remaining] Chain 3: 50% [0:00:02 of 0:00:03 remaining] Chain 3: 60% [0:00:01 of 0:00:03 remaining] Chain 3: 70% [0:00:01 of 0:00:03 remaining] Chain 3: 80% [0:00:01 of 0:00:04 remaining] Chain 3: 90% [0:00:00 of 0:00:04 remaining] Chain 3: 100% [0:00:00 of 0:00:04 remaining] MCMC Simulation of 10000 Iterations x 3 Chains... Chain 1: 0% [0:01:16 of 0:01:16 remaining] Chain 1: 10% [0:00:06 of 0:00:07 remaining] Chain 1: 20% [0:00:05 of 0:00:06 remaining] Chain 1: 30% [0:00:04 of 0:00:06 remaining] Chain 1: 40% [0:00:03 of 0:00:06 remaining] Chain 1: 50% [0:00:03 of 0:00:06 remaining] Chain 1: 60% [0:00:02 of 0:00:06 remaining] Chain 1: 70% [0:00:02 of 0:00:06 remaining] Chain 1: 80% [0:00:01 of 0:00:06 remaining] Chain 1: 90% [0:00:01 of 0:00:06 remaining] Chain 1: 100% [0:00:00 of 0:00:06 remaining] Chain 2: 0% [0:00:07 of 0:00:07 remaining] Chain 2: 10% [0:00:06 of 0:00:07 remaining] Chain 2: 20% [0:00:06 of 0:00:07 remaining] Chain 2: 30% [0:00:05 of 0:00:07 remaining] Chain 2: 40% [0:00:04 of 0:00:07 remaining] Chain 2: 50% [0:00:03 of 0:00:07 remaining] Chain 2: 60% [0:00:03 of 0:00:07 remaining] Chain 2: 70% [0:00:02 of 0:00:07 remaining] Chain 2: 80% [0:00:01 of 0:00:07 remaining] Chain 2: 90% [0:00:01 of 0:00:07 remaining] Chain 2: 100% [0:00:00 of 0:00:07 remaining] Chain 3: 0% [0:00:11 of 0:00:11 remaining] Chain 3: 10% [0:00:05 of 0:00:06 remaining] Chain 3: 20% [0:00:04 of 0:00:05 remaining] Chain 3: 30% [0:00:04 of 0:00:05 remaining] Chain 3: 40% [0:00:03 of 0:00:05 remaining] Chain 3: 50% [0:00:03 of 0:00:05 remaining] Chain 3: 60% [0:00:02 of 0:00:05 remaining] Chain 3: 70% [0:00:02 of 0:00:05 remaining] Chain 3: 80% [0:00:01 of 0:00:05 remaining] Chain 3: 90% [0:00:01 of 0:00:05 remaining] Chain 3: 100% [0:00:00 of 0:00:05 remaining] MCMC Simulation of 10000 Iterations x 3 Chains... Chain 1: 0% [2:59:41 of 2:59:52 remaining] Chain 1: 10% [0:01:37 of 0:01:48 remaining] Chain 1: 20% [0:00:44 of 0:00:54 remaining] Chain 1: 30% [0:00:25 of 0:00:36 remaining] Chain 1: 40% [0:00:16 of 0:00:27 remaining] Chain 1: 50% [0:00:11 of 0:00:22 remaining] Chain 1: 60% [0:00:07 of 0:00:18 remaining] Chain 1: 70% [0:00:05 of 0:00:16 remaining] Chain 1: 80% [0:00:03 of 0:00:14 remaining] Chain 1: 90% [0:00:01 of 0:00:12 remaining] Chain 1: 100% [0:00:00 of 0:00:11 remaining] Chain 2: 0% [0:00:00 of 0:00:00 remaining] Chain 2: 10% [0:00:00 of 0:00:00 remaining] Chain 2: 20% [0:00:00 of 0:00:00 remaining] Chain 2: 30% [0:00:00 of 0:00:00 remaining] Chain 2: 40% [0:00:00 of 0:00:00 remaining] Chain 2: 50% [0:00:00 of 0:00:00 remaining] Chain 2: 60% [0:00:00 of 0:00:00 remaining] Chain 2: 70% [0:00:00 of 0:00:01 remaining] Chain 2: 80% [0:00:00 of 0:00:00 remaining] Chain 2: 90% [0:00:00 of 0:00:00 remaining] Chain 2: 100% [0:00:00 of 0:00:00 remaining] Chain 3: 0% [0:00:00 of 0:00:00 remaining] Chain 3: 10% [0:00:00 of 0:00:00 remaining] Chain 3: 20% [0:00:00 of 0:00:00 remaining] Chain 3: 30% [0:00:00 of 0:00:00 remaining] Chain 3: 40% [0:00:00 of 0:00:00 remaining] Chain 3: 50% [0:00:00 of 0:00:00 remaining] Chain 3: 60% [0:00:00 of 0:00:00 remaining] Chain 3: 70% [0:00:00 of 0:00:00 remaining] Chain 3: 80% [0:00:00 of 0:00:00 remaining] Chain 3: 90% [0:00:00 of 0:00:00 remaining] Chain 3: 100% [0:00:00 of 0:00:00 remaining] Iterations = 252:10000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 4875 Gelman, Rubin, and Brooks Diagnostic: PSRF 97.5% s2 1.032 1.072 beta[1] 1.034 1.040 beta[2] 1.030 1.036 Multivariate 1.029 NaN Iterations = 252:10000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 4875 Geweke Diagnostic: First Window Fraction = 0.1 Second Window Fraction = 0.5 Z-score p-value s2 -0.636 0.5249 beta[1] -0.094 0.9249 beta[2] -0.340 0.7340 Z-score p-value s2 0.094 0.9252 beta[1] 0.490 0.6241 beta[2] -0.312 0.7547 Z-score p-value s2 -1.101 0.2707 beta[1] 0.082 0.9345 beta[2] -0.321 0.7479 Iterations = 252:10000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 4875 Heidelberger and Welch Diagnostic: Target Halfwidth Ratio = 0.1 Alpha = 0.05 Burn-in Stationarity p-value Mean Halfwidth Test s2 251 1 0.4710 1.14456117 0.181613013 0 beta[1] 251 1 0.6523 0.61714611 0.063647552 0 beta[2] 251 1 0.7394 0.79018718 0.017723977 1 Burn-in Stationarity p-value Mean Halfwidth Test s2 1225 1 0.2870 1.02668010 0.20050240 0 beta[1] 251 1 0.4682 0.50099131 0.22963497 0 beta[2] 251 1 0.4472 0.82313805 0.06730049 1 Burn-in Stationarity p-value Mean Halfwidth Test s2 251 1 0.6255 2.68566986 2.730461956 0 beta[1] 251 1 0.4435 0.63824537 0.088470973 0 beta[2] 251 1 0.4102 0.78700111 0.024685494 1 Iterations = 252:10000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 4875 Raftery and Lewis Diagnostic: Quantile (q) = 0.025 Accuracy (r) = 0.005 Probability (s) = 0.95 Thinning Burn-in Total Nmin Dependence Factor s2 2 257 8.4090×10³ 3746 2.2447944 beta[1] 4 271 2.3303×10⁴ 3746 6.2207688 beta[2] 4 271 2.2791×10⁴ 3746 6.0840897 Thinning Burn-in Total Nmin Dependence Factor s2 2 257 8.5470×10³ 3746 2.2816337 beta[1] 4 311 6.6995×10⁴ 3746 17.8844100 beta[2] 4 315 6.8787×10⁴ 3746 18.3627870 Thinning Burn-in Total Nmin Dependence Factor s2 2 257 8.4810×10³ 3746 2.2640149 beta[1] 2 269 2.0647×10⁴ 3746 5.5117459 beta[2] 8 291 4.6563×10⁴ 3746 12.4300587 Iterations = 252:10000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 4875 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS s2 1.7176649 4.12420057 0.0341029415 0.3086724048 178.5188 beta[1] 0.5854609 1.35669661 0.0112185002 0.0360900147 1413.1601 beta[2] 0.8001088 0.40613168 0.0033582957 0.0103093396 1551.9297 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% s2 0.167822814 0.380087312 0.68160955 1.3731913 12.2575075 beta[1] -1.866765327 0.020518063 0.57594853 1.1390496 3.1169593 beta[2] 0.031699995 0.631823681 0.80354149 0.9756752 1.5435092 95% Lower 95% Upper s2 0.071095609 5.3356949 beta[1] -1.906093882 3.0576759 beta[2] 0.012003909 1.5152581 s2 beta[1] beta[2] s2 1.000000000 0.010892553 -0.019504786 beta[1] 0.010892553 1.000000000 -0.901756663 beta[2] -0.019504786 -0.901756663 1.000000000 Lag 2 Lag 10 Lag 20 Lag 100 s2 0.78891772 0.328481634 0.082559341 -0.01371176877 beta[1] 0.33010573 -0.022330966 -0.027598651 0.01354435891 beta[2] 0.27375689 -0.013901598 -0.007929276 0.00009746474 Lag 2 Lag 10 Lag 20 Lag 100 s2 0.92616783 0.78428150 0.68073772 0.044490794 beta[1] 0.67385410 0.39574953 0.29903628 0.099763498 beta[2] 0.61567919 0.36709799 0.28916191 0.097538475 Lag 2 Lag 10 Lag 20 Lag 100 s2 0.98496902 0.940153196 0.8960440955 0.58088452 beta[1] 0.25193107 0.045264183 -0.0044392196 0.04061188 beta[2] 0.23696006 0.040870460 0.0028880229 0.01483408 Change Rate s2 1.000 beta[1] 0.597 beta[2] 0.597 Multivariate 1.000 MCMC Processing of 4875 Iterations x 3 Chains... Chain 1: 0% [0:13:52 of 0:13:53 remaining] Chain 1: 10% [0:00:15 of 0:00:17 remaining] Chain 1: 20% [0:00:07 of 0:00:09 remaining] Chain 1: 30% [0:00:04 of 0:00:06 remaining] Chain 1: 40% [0:00:03 of 0:00:04 remaining] Chain 1: 50% [0:00:02 of 0:00:03 remaining] Chain 1: 60% [0:00:01 of 0:00:03 remaining] Chain 1: 70% [0:00:01 of 0:00:02 remaining] Chain 1: 80% [0:00:00 of 0:00:02 remaining] Chain 1: 90% [0:00:00 of 0:00:02 remaining] Chain 1: 100% [0:00:00 of 0:00:02 remaining] Chain 2: 0% [0:14:15 of 0:14:17 remaining] Chain 2: 10% [0:00:16 of 0:00:18 remaining] Chain 2: 20% [0:00:07 of 0:00:09 remaining] Chain 2: 30% [0:00:04 of 0:00:06 remaining] Chain 2: 40% [0:00:03 of 0:00:04 remaining] Chain 2: 50% [0:00:02 of 0:00:04 remaining] Chain 2: 60% [0:00:01 of 0:00:03 remaining] Chain 2: 70% [0:00:01 of 0:00:03 remaining] Chain 2: 80% [0:00:00 of 0:00:02 remaining] Chain 2: 90% [0:00:00 of 0:00:02 remaining] Chain 2: 100% [0:00:00 of 0:00:02 remaining] Chain 3: 0% [0:14:38 of 0:14:40 remaining] Chain 3: 10% [0:00:16 of 0:00:18 remaining] Chain 3: 20% [0:00:07 of 0:00:09 remaining] Chain 3: 30% [0:00:04 of 0:00:06 remaining] Chain 3: 40% [0:00:03 of 0:00:05 remaining] Chain 3: 50% [0:00:02 of 0:00:04 remaining] Chain 3: 60% [0:00:01 of 0:00:03 remaining] Chain 3: 70% [0:00:01 of 0:00:03 remaining] Chain 3: 80% [0:00:00 of 0:00:02 remaining] Chain 3: 90% [0:00:00 of 0:00:02 remaining] Chain 3: 100% [0:00:00 of 0:00:02 remaining] DIC Effective Parameters pD 13.072190 0.12294518 pV 28.332460 7.75308021 Iterations = 1000:5000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 2001 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS beta[1] 0.61634233 1.12905687 0.014572419 0.036059088 980.3974 beta[2] 0.79049024 0.33834357 0.004366905 0.010670782 1005.3641 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% beta[1] -1.61264765 0.050151786 0.62426495 1.1369925 2.7429129 beta[2] 0.14028405 0.625397684 0.79790890 0.9575674 1.4191171 MCMC Simulation of 5000 Iterations x 3 Chains... Chain 1: 0% [0:00:11 of 0:00:11 remaining] Chain 1: 10% [0:00:02 of 0:00:02 remaining] Chain 1: 20% [0:00:02 of 0:00:03 remaining] Chain 1: 30% [0:00:02 of 0:00:02 remaining] Chain 1: 40% [0:00:01 of 0:00:02 remaining] Chain 1: 50% [0:00:01 of 0:00:02 remaining] Chain 1: 60% [0:00:01 of 0:00:02 remaining] Chain 1: 70% [0:00:01 of 0:00:02 remaining] Chain 1: 80% [0:00:00 of 0:00:02 remaining] Chain 1: 90% [0:00:00 of 0:00:02 remaining] Chain 1: 100% [0:00:00 of 0:00:02 remaining] Chain 2: 0% [0:00:01 of 0:00:01 remaining] Chain 2: 10% [0:00:01 of 0:00:01 remaining] Chain 2: 20% [0:00:01 of 0:00:01 remaining] Chain 2: 30% [0:00:01 of 0:00:01 remaining] Chain 2: 40% [0:00:01 of 0:00:01 remaining] Chain 2: 50% [0:00:00 of 0:00:01 remaining] Chain 2: 60% [0:00:00 of 0:00:01 remaining] Chain 2: 70% [0:00:00 of 0:00:01 remaining] Chain 2: 80% [0:00:00 of 0:00:01 remaining] Chain 2: 90% [0:00:00 of 0:00:01 remaining] Chain 2: 100% [0:00:00 of 0:00:01 remaining] Chain 3: 0% [0:00:01 of 0:00:01 remaining] Chain 3: 10% [0:00:01 of 0:00:02 remaining] Chain 3: 20% [0:00:01 of 0:00:02 remaining] Chain 3: 30% [0:00:01 of 0:00:02 remaining] Chain 3: 40% [0:00:01 of 0:00:02 remaining] Chain 3: 50% [0:00:01 of 0:00:02 remaining] Chain 3: 60% [0:00:01 of 0:00:02 remaining] Chain 3: 70% [0:00:01 of 0:00:02 remaining] Chain 3: 80% [0:00:00 of 0:00:02 remaining] Chain 3: 90% [0:00:00 of 0:00:02 remaining] Chain 3: 100% [0:00:00 of 0:00:02 remaining] Iterations = 252:15000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 7375 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS s2 1.6053142 3.56395786 0.0239602266 0.210080102 287.80298 beta[1] 0.5679749 1.33337848 0.0089642054 0.029045407 2107.42418 beta[2] 0.8059035 0.39919175 0.0026837368 0.008311868 2306.56557 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% s2 0.170484924 0.3897077241 0.6943026 1.3816604 9.6892161 beta[1] -1.984668634 -0.0073908353 0.5537523 1.1421998 3.0859019 beta[2] 0.046191143 0.6307741418 0.8100425 0.9853930 1.5809317 Object of type "Model" ------------------------------------------------------------------------------- beta: A monitored node of type "2-element ArrayStochastic{1}" [-0.6457306721039767, -1.4632513788889214] ------------------------------------------------------------------------------- y: An unmonitored node of type "5-element ArrayStochastic{1}" [1.0, 3.0, 3.0, 3.0, 5.0] ------------------------------------------------------------------------------- mu: An unmonitored node of type "5-element ArrayLogical{1}" [-2.108982050992898, -3.5722334298818197, -5.0354848087707405, -6.498736187659662, -7.961987566548584] ------------------------------------------------------------------------------- xmat: [1.0 1.0; 1.0 2.0; 1.0 3.0; 1.0 4.0; 1.0 5.0] ------------------------------------------------------------------------------- s2: A monitored node of type "ScalarStochastic" 1.0083623637301151 >>> Testing ../doc/samplers/amm.jl ┌ Warning: `cholesky(A::Union{StridedMatrix, RealHermSymComplexHerm{<:Real, <:StridedMatrix}}, ::Val{true}; tol = 0.0, check::Bool = true)` is deprecated, use `cholesky(A, RowMaximum(); tol, check)` instead. │ caller = sample!(v::AMMVariate, logf::var"#33#34"; adapt::Bool) at amm.jl:81 └ @ Mamba ~/.julia/packages/Mamba/bdnmz/src/samplers/amm.jl:81 Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.53565402 1.09143497 0.015435221 0.063861231 292.09300 b1 0.82549493 0.33079524 0.004678151 0.017995392 337.90597 s2 1.12674350 1.63325552 0.023097721 0.094087470 301.33161 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -1.66713417 -0.02880850 0.58578355 1.0748150 2.6625235 b1 0.21058812 0.66492240 0.80959551 0.9794734 1.5141445 s2 0.17046577 0.37456646 0.67945669 1.2258547 5.1089953 >>> Testing ../doc/samplers/amwg.jl Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.75418895 1.2668141 0.017915456 0.123074511 105.94721 b1 0.75082315 0.3926535 0.005552959 0.038570546 103.63522 s2 1.49105254 2.3447403 0.033159635 0.149777472 245.07358 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -1.9402666317 0.06760841 0.8195892 1.44361704 3.1526976 b1 0.0014572882 0.54381824 0.7205169 0.96856755 1.5591978 s2 0.1760167369 0.43242788 0.7931340 1.56195640 7.2902614 >>> Testing ../doc/samplers/bhmc.jl Iterations = 1:10000 Thinning interval = 1 Chains = 1 Samples per chain = 10000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS gamma[1] 0.7623 0.42569570 0.0042569570 0.0048468567 7713.974 gamma[2] 0.9067 0.29086693 0.0029086693 0.0035705162 6636.310 gamma[3] 0.0000 0.00000000 0.0000000000 0.0000000000 NaN gamma[4] 0.0000 0.00000000 0.0000000000 0.0000000000 NaN gamma[5] 0.4015 0.49022626 0.0049022626 0.0049815315 9684.281 gamma[6] 0.1961 0.39706493 0.0039706493 0.0044966767 7797.219 gamma[7] 0.5017 0.50002211 0.0050002211 0.0032444358 10000.000 gamma[8] 0.6430 0.47913877 0.0047913877 0.0044528960 10000.000 gamma[9] 0.4784 0.49955820 0.0049955820 0.0032465055 10000.000 gamma[10] 0.2013 0.40099176 0.0040099176 0.0045006285 7938.246 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% gamma[1] 0 1 1 1 1 gamma[2] 0 1 1 1 1 gamma[3] 0 0 0 0 0 gamma[4] 0 0 0 0 0 gamma[5] 0 0 0 1 1 gamma[6] 0 0 0 0 1 gamma[7] 0 0 1 1 1 gamma[8] 0 0 1 1 1 gamma[9] 0 0 0 1 1 gamma[10] 0 0 0 0 1 >>> Testing ../doc/samplers/bia.jl Iterations = 1:10000 Thinning interval = 1 Chains = 1 Samples per chain = 10000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS gamma[1] 0.0101 0.099994949 0.00099994949 0.0037321833 717.84422 gamma[2] 0.0402 0.196437824 0.00196437824 0.0191548607 105.17009 gamma[3] 0.9984 0.039969986 0.00039969986 0.0016000000 624.06241 gamma[4] 0.9992 0.028274369 0.00028274369 0.0008000000 1249.12491 gamma[5] 0.7169 0.450527122 0.00450527122 0.0157047860 822.95831 gamma[6] 0.1651 0.371289342 0.00371289342 0.0165208745 505.07853 gamma[7] 0.9569 0.203092380 0.00203092380 0.0191489088 112.48619 gamma[8] 0.0129 0.112848852 0.00112848852 0.0091223945 153.03001 gamma[9] 0.4543 0.497932027 0.00497932027 0.0151352522 1082.33275 gamma[10] 0.4821 0.499704473 0.00499704473 0.0234517751 454.02005 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% gamma[1] 0 0 0 0 0 gamma[2] 0 0 0 0 1 gamma[3] 1 1 1 1 1 gamma[4] 1 1 1 1 1 gamma[5] 0 0 1 1 1 gamma[6] 0 0 0 0 1 gamma[7] 0 1 1 1 1 gamma[8] 0 0 0 0 0 gamma[9] 0 0 0 1 1 gamma[10] 0 0 0 1 1 >>> Testing ../doc/samplers/bmc3.jl Iterations = 1:10000 Thinning interval = 1 Chains = 1 Samples per chain = 10000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS gamma[1] 0.3815 0.48577911 0.0048577911 0.0171207093 805.07098 gamma[2] 0.0000 0.00000000 0.0000000000 0.0000000000 NaN gamma[3] 0.0047 0.06839867 0.0006839867 0.0047000000 211.78714 gamma[4] 0.5357 0.49874882 0.0049874882 0.0142858657 1218.85104 gamma[5] 0.0000 0.00000000 0.0000000000 0.0000000000 NaN gamma[6] 0.9652 0.18328208 0.0018328208 0.0067322231 741.17879 gamma[7] 0.8524 0.35472077 0.0035472077 0.0148386065 571.46152 gamma[8] 0.0000 0.00000000 0.0000000000 0.0000000000 NaN gamma[9] 0.9985 0.03870271 0.0003870271 0.0011135075 1208.08211 gamma[10] 0.0000 0.00000000 0.0000000000 0.0000000000 NaN Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% gamma[1] 0 0 0 1 1 gamma[2] 0 0 0 0 0 gamma[3] 0 0 0 0 0 gamma[4] 0 0 1 1 1 gamma[5] 0 0 0 0 0 gamma[6] 0 1 1 1 1 gamma[7] 0 1 1 1 1 gamma[8] 0 0 0 0 0 gamma[9] 1 1 1 1 1 gamma[10] 0 0 0 0 0 Iterations = 1:10000 Thinning interval = 1 Chains = 1 Samples per chain = 10000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS gamma[1] 0.4062 0.491147312 0.00491147312 0.0144912234 1148.71804 gamma[2] 0.0000 0.000000000 0.00000000000 0.0000000000 NaN gamma[3] 0.0000 0.000000000 0.00000000000 0.0000000000 NaN gamma[4] 0.5056 0.499993639 0.00499993639 0.0144350303 1199.75773 gamma[5] 0.0000 0.000000000 0.00000000000 0.0000000000 NaN gamma[6] 0.9473 0.223445078 0.00223445078 0.0114458364 381.10664 gamma[7] 0.8429 0.363913222 0.00363913222 0.0129203426 793.31855 gamma[8] 0.0000 0.000000000 0.00000000000 0.0000000000 NaN gamma[9] 0.9975 0.049939958 0.00049939958 0.0017943514 774.60687 gamma[10] 0.0000 0.000000000 0.00000000000 0.0000000000 NaN Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% gamma[1] 0 0 0 1 1 gamma[2] 0 0 0 0 0 gamma[3] 0 0 0 0 0 gamma[4] 0 0 1 1 1 gamma[5] 0 0 0 0 0 gamma[6] 0 1 1 1 1 gamma[7] 0 1 1 1 1 gamma[8] 0 0 0 0 0 gamma[9] 1 1 1 1 1 gamma[10] 0 0 0 0 0 >>> Testing ../doc/samplers/bmg.jl Iterations = 1:10000 Thinning interval = 1 Chains = 1 Samples per chain = 10000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS gamma[1] 0.0186 0.135114269 0.00135114269 0.005996666 507.67146 gamma[2] 0.4301 0.495114637 0.00495114637 0.021605973 525.12641 gamma[3] 0.0000 0.000000000 0.00000000000 0.000000000 NaN gamma[4] 0.5390 0.498501605 0.00498501605 0.024035538 430.15546 gamma[5] 1.0000 0.000000000 0.00000000000 0.000000000 NaN gamma[6] 0.3778 0.484861495 0.00484861495 0.021537180 506.82466 gamma[7] 0.0000 0.000000000 0.00000000000 0.000000000 NaN gamma[8] 0.9994 0.024488772 0.00024488772 0.000600000 1665.83325 gamma[9] 0.0395 0.194791027 0.00194791027 0.008806993 489.19562 gamma[10] 0.9987 0.036033871 0.00036033871 0.001300000 768.30760 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% gamma[1] 0 0 0 0 0 gamma[2] 0 0 0 1 1 gamma[3] 0 0 0 0 0 gamma[4] 0 0 1 1 1 gamma[5] 1 1 1 1 1 gamma[6] 0 0 0 1 1 gamma[7] 0 0 0 0 0 gamma[8] 1 1 1 1 1 gamma[9] 0 0 0 0 1 gamma[10] 1 1 1 1 1 Iterations = 1:10000 Thinning interval = 1 Chains = 1 Samples per chain = 10000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS gamma[1] 0.0215 0.145051212 0.00145051212 0.0052385035 766.70490 gamma[2] 0.4463 0.497132803 0.00497132803 0.0241939365 422.21316 gamma[3] 0.0000 0.000000000 0.00000000000 0.0000000000 NaN gamma[4] 0.5439 0.498093967 0.00498093967 0.0234868951 449.75018 gamma[5] 0.9996 0.019996999 0.00019996999 0.0004000000 2499.24992 gamma[6] 0.4172 0.493121158 0.00493121158 0.0201810392 597.06309 gamma[7] 0.0000 0.000000000 0.00000000000 0.0000000000 NaN gamma[8] 0.9995 0.022356207 0.00022356207 0.0005000000 1999.19992 gamma[9] 0.0353 0.184546243 0.00184546243 0.0080094073 530.89624 gamma[10] 1.0000 0.000000000 0.00000000000 0.0000000000 NaN Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% gamma[1] 0 0 0 0 0 gamma[2] 0 0 0 1 1 gamma[3] 0 0 0 0 0 gamma[4] 0 0 1 1 1 gamma[5] 1 1 1 1 1 gamma[6] 0 0 0 1 1 gamma[7] 0 0 0 0 0 gamma[8] 1 1 1 1 1 gamma[9] 0 0 0 0 1 gamma[10] 1 1 1 1 1 >>> Testing ../doc/samplers/hmc.jl Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.63838564 1.34408499 0.019008232 0.032297395 1731.8827 b1 0.78232565 0.52641753 0.007444668 0.014249507 1364.7749 s2 2.57215610 46.96973119 0.664252309 0.949186117 2448.6877 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -1.897088749 -0.004252836 0.62742043 1.21231971 3.1990055 b1 -0.036886663 0.611705596 0.80033636 0.99030403 1.5673038 s2 0.147962718 0.408223497 0.75088105 1.50666125 8.3765350 Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.61621807 1.33193395 0.0188363906 0.029719461 2008.5541 b1 0.79224988 0.42990798 0.0060798169 0.008386167 2627.9917 s2 1.78745700 11.82932130 0.1672918661 0.298770718 1567.6301 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -1.844229593 0.033352106 0.6256058 1.1973331 3.170734 b1 0.024567896 0.614993619 0.7928512 0.9783479 1.557293 s2 0.186180072 0.402678924 0.7080929 1.3555597 7.007014 >>> Testing ../doc/samplers/mala.jl Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.51796284 1.0740183 0.0151889127 0.128901846 69.42328 b1 0.80733486 0.3370974 0.0047672774 0.036329285 86.09881 s2 1.34193688 2.2195483 0.0313891532 0.194830329 129.78248 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -1.97791109 -0.06373933 0.67753333 1.2775544 2.3296949 b1 0.21709563 0.59056012 0.77805793 0.9847964 1.5562653 s2 0.17532035 0.41881349 0.70444692 1.2332358 6.5640326 Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.70818098 0.82117936 0.011613230 0.088318488 86.451554 b1 0.66934076 0.33694935 0.004765183 0.038901136 75.024774 s2 1.13733496 1.42303938 0.020124816 0.132091747 116.059974 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -0.75627144 0.07855082 0.66717625 1.2241551 2.5963911 b1 0.00000000 0.46396600 0.72413005 0.8885923 1.2267213 s2 0.18759769 0.53018187 0.80589577 1.1548194 4.5861949 >>> Testing ../doc/samplers/nuts.jl Iterations = 1001:5000 Thinning interval = 1 Chains = 1 Samples per chain = 4000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.68691706 2.23283203 0.035304174 0.15253526 214.27506 b1 0.78209240 0.66214189 0.010469382 0.04390254 227.46934 s2 2.31616436 14.48724152 0.229063401 0.84172788 296.22927 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -1.95345778 0.15289398 0.70373220 1.3234183 3.3781563 b1 -0.03165222 0.58012172 0.78708178 0.9337171 1.5476620 s2 0.21417194 0.38127349 0.66554567 1.3334196 9.9721729 >>> Testing ../doc/samplers/rwm.jl Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.77566655 0.89017508 0.0125889767 0.104022384 73.231387 b1 0.74280577 0.28477208 0.0040272854 0.030415011 87.663512 s2 1.20599307 2.59939620 0.0367610136 0.194228824 179.109078 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -1.01563165 0.23268928 0.7388095 1.2960081 2.5975415 b1 0.12566449 0.58506436 0.7550384 0.9166477 1.2807745 s2 0.17741566 0.37251607 0.6568325 1.2268229 5.4667917 >>> Testing ../doc/samplers/slice.jl Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 0.48504898 1.03910872 0.0146952164 0.102780015 102.21264 b1 0.83069791 0.32343078 0.0045740019 0.029445435 120.64984 s2 1.20316688 1.89126975 0.0267465933 0.108472148 303.99782 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -1.86517620 0.005626099 0.56539253 1.13647265 2.1929041 b1 0.29937667 0.634658588 0.80912120 0.98076846 1.6000293 s2 0.17323735 0.380418052 0.64792268 1.19787637 5.8607587 Iterations = 1:5000 Thinning interval = 1 Chains = 1 Samples per chain = 5000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS b0 1.0076341 1.3179661 0.018638855 0.158600681 69.05552 b1 0.6862908 0.3787316 0.005356074 0.042164143 80.68198 s2 1.4868305 3.0132239 0.042613421 0.274716172 120.30796 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% b0 -0.85435428 0.23717138 0.76158932 1.3737889 5.0323961 b1 -0.38723995 0.56891804 0.75348221 0.9011153 1.2519594 s2 0.16476997 0.35502177 0.60758275 1.2595977 9.5173453 >>> Testing ../doc/samplers/slicesimplex.jl Iterations = 1:10000 Thinning interval = 1 Chains = 1 Samples per chain = 10000 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS rho[1] 0.047680375 0.022140296 0.00022140296 0.00036365653 3706.6713 rho[2] 0.170301079 0.039570317 0.00039570317 0.00103326416 1466.6157 rho[3] 0.524334770 0.052369883 0.00052369883 0.00165492520 1001.3974 rho[4] 0.133816746 0.036856156 0.00036856156 0.00093486433 1554.2573 rho[5] 0.123867030 0.035413750 0.00035413750 0.00088101650 1615.7559 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% rho[1] 0.014602177 0.031397706 0.04442876 0.06030344 0.09990222 rho[2] 0.100757202 0.142099881 0.16689924 0.19583308 0.25383423 rho[3] 0.418497963 0.489907193 0.52400646 0.55958602 0.62620952 rho[4] 0.071673299 0.106848947 0.13082923 0.15792508 0.21438151 rho[5] 0.064865909 0.097410676 0.12105461 0.14557080 0.20089345 >>> Testing ../doc/mcmc/discretediag.jl >>> Testing ../doc/mcmc/readcoda.jl Iterations = 1:200 Thinning interval = 1 Chains = 1,2 Samples per chain = 200 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS alpha 3.0025394 0.53475753 0.026737877 0.018902157 200.00000 beta 0.8013086 0.39267477 0.019633739 0.030895834 161.53482 sigma 1.0821777 0.94869150 0.047434575 0.061191837 200.00000 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% alpha 1.8322542 2.751095 3.0257 3.2709700 3.9511365 beta -0.0125375 0.599750 0.8065 1.0079525 1.5292802 sigma 0.4329000 0.625000 0.8360 1.2378125 2.8597185 >>> Testing ../doc/mcmc/newunivardist.jl MCMC Simulation of 10000 Iterations x 3 Chains... Chain 1: 0% [0:00:60 of 0:00:60 remaining] Chain 1: 10% [0:00:04 of 0:00:05 remaining] Chain 1: 20% [0:00:04 of 0:00:05 remaining] Chain 1: 30% [0:00:03 of 0:00:04 remaining] Chain 1: 40% [0:00:03 of 0:00:05 remaining] Chain 1: 50% [0:00:02 of 0:00:05 remaining] Chain 1: 60% [0:00:02 of 0:00:04 remaining] Chain 1: 70% [0:00:01 of 0:00:04 remaining] Chain 1: 80% [0:00:01 of 0:00:04 remaining] Chain 1: 90% [0:00:00 of 0:00:04 remaining] Chain 1: 100% [0:00:00 of 0:00:04 remaining] Chain 2: 0% [0:00:03 of 0:00:03 remaining] Chain 2: 10% [0:00:03 of 0:00:04 remaining] Chain 2: 20% [0:00:03 of 0:00:04 remaining] Chain 2: 30% [0:00:03 of 0:00:04 remaining] Chain 2: 40% [0:00:02 of 0:00:04 remaining] Chain 2: 50% [0:00:02 of 0:00:04 remaining] Chain 2: 60% [0:00:02 of 0:00:04 remaining] Chain 2: 70% [0:00:01 of 0:00:04 remaining] Chain 2: 80% [0:00:01 of 0:00:04 remaining] Chain 2: 90% [0:00:00 of 0:00:04 remaining] Chain 2: 100% [0:00:00 of 0:00:04 remaining] Chain 3: 0% [0:00:06 of 0:00:06 remaining] Chain 3: 10% [0:00:04 of 0:00:04 remaining] Chain 3: 20% [0:00:03 of 0:00:04 remaining] Chain 3: 30% [0:00:03 of 0:00:04 remaining] Chain 3: 40% [0:00:02 of 0:00:04 remaining] Chain 3: 50% [0:00:02 of 0:00:04 remaining] Chain 3: 60% [0:00:01 of 0:00:04 remaining] Chain 3: 70% [0:00:01 of 0:00:04 remaining] Chain 3: 80% [0:00:01 of 0:00:04 remaining] Chain 3: 90% [0:00:00 of 0:00:03 remaining] Chain 3: 100% [0:00:00 of 0:00:03 remaining] Iterations = 252:10000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 4875 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS s2 1.19619781 1.75330200 0.0144980232 0.073177567 574.0603 beta[1] 0.60895984 1.13390742 0.0093762604 0.015200006 4875.0000 beta[2] 0.79987040 0.34153855 0.0028241762 0.004196895 4875.0000 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% s2 0.16877750 0.388117964 0.6652748 1.2690116 5.9210058 beta[1] -1.61905275 0.042584316 0.5903660 1.1544115 2.9446370 beta[2] 0.09869691 0.632179942 0.8015285 0.9734778 1.4642104 >>> Testing ../doc/mcmc/newmultivardist.jl WARNING: replacing module Testing. MCMC Simulation of 10000 Iterations x 3 Chains... Chain 1: 0% [0:04:24 of 0:04:24 remaining] Chain 1: 10% [0:00:06 of 0:00:07 remaining] Chain 1: 20% [0:00:04 of 0:00:05 remaining] Chain 1: 30% [0:00:03 of 0:00:05 remaining] Chain 1: 40% [0:00:03 of 0:00:04 remaining] Chain 1: 50% [0:00:02 of 0:00:04 remaining] Chain 1: 60% [0:00:02 of 0:00:04 remaining] Chain 1: 70% [0:00:01 of 0:00:04 remaining] Chain 1: 80% [0:00:01 of 0:00:04 remaining] Chain 1: 90% [0:00:00 of 0:00:04 remaining] Chain 1: 100% [0:00:00 of 0:00:04 remaining] Chain 2: 0% [0:00:03 of 0:00:03 remaining] Chain 2: 10% [0:00:05 of 0:00:05 remaining] Chain 2: 20% [0:00:03 of 0:00:04 remaining] Chain 2: 30% [0:00:03 of 0:00:04 remaining] Chain 2: 40% [0:00:02 of 0:00:04 remaining] Chain 2: 50% [0:00:02 of 0:00:04 remaining] Chain 2: 60% [0:00:01 of 0:00:04 remaining] Chain 2: 70% [0:00:01 of 0:00:04 remaining] Chain 2: 80% [0:00:01 of 0:00:04 remaining] Chain 2: 90% [0:00:00 of 0:00:04 remaining] Chain 2: 100% [0:00:00 of 0:00:04 remaining] Chain 3: 0% [0:00:03 of 0:00:03 remaining] Chain 3: 10% [0:00:03 of 0:00:03 remaining] Chain 3: 20% [0:00:03 of 0:00:04 remaining] Chain 3: 30% [0:00:03 of 0:00:04 remaining] Chain 3: 40% [0:00:02 of 0:00:04 remaining] Chain 3: 50% [0:00:02 of 0:00:04 remaining] Chain 3: 60% [0:00:02 of 0:00:04 remaining] Chain 3: 70% [0:00:01 of 0:00:04 remaining] Chain 3: 80% [0:00:01 of 0:00:04 remaining] Chain 3: 90% [0:00:00 of 0:00:04 remaining] Chain 3: 100% [0:00:00 of 0:00:04 remaining] Iterations = 252:10000 Thinning interval = 2 Chains = 1,2,3 Samples per chain = 4875 Empirical Posterior Estimates: Mean SD Naive SE MCSE ESS s2 1.40151127 2.5583481 0.0211549349 0.1668699855 235.05138 beta[1] 0.59725818 1.2734020 0.0105297384 0.0179241783 4875.00000 beta[2] 0.80141515 0.3842296 0.0031771878 0.0048940037 4875.00000 Quantiles: 2.5% 25.0% 50.0% 75.0% 97.5% s2 0.17421247 0.396539038 0.69674273 1.3637117 7.0063817 beta[1] -1.81510480 -0.014227261 0.59208629 1.2017353 3.0680186 beta[2] 0.05155518 0.621828448 0.80161165 0.9816074 1.5417761 Testing Mamba tests passed Testing completed after 350.85s PkgEval succeeded after 466.53s