Package evaluation to test JWAS on Julia 1.14.0-DEV.2168 (2569364ac4*) started at 2026-05-11T03:02:49.064 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 14.5s ################################################################################ # Installation # Installing JWAS... Resolving package versions... Installed libaom_jll ─────────────────── v3.13.3+0 Installed libass_jll ─────────────────── v0.17.4+0 Installed HarfBuzz_jll ───────────────── v8.5.1+0 Installed Bzip2_jll ──────────────────── v1.0.9+0 Installed PlotThemes ─────────────────── v3.3.0 Installed Libuuid_jll ────────────────── v2.42.0+0 Installed Libtiff_jll ────────────────── v4.7.2+0 Installed Qt6Base_jll ────────────────── v6.10.2+1 Installed Cairo_jll ──────────────────── v1.18.7+0 Installed InlineStrings ──────────────── v1.4.5 Installed Xorg_libXinerama_jll ───────── v1.1.7+0 Installed OrderedCollections ─────────── v1.8.1 Installed libinput_jll ───────────────── v1.28.1+0 Installed SentinelArrays ─────────────── v1.4.9 Installed Graphite2_jll ──────────────── v1.3.15+0 Installed Xorg_libpciaccess_jll ──────── v0.19.0+0 Installed Fontconfig_jll ─────────────── v2.17.1+0 Installed Compiler ───────────────────── v0.1.1 Installed fzf_jll ────────────────────── v0.61.1+0 Installed Opus_jll ───────────────────── v1.6.1+0 Installed OpenSpecFun_jll ────────────── v0.5.6+0 Installed Tables ─────────────────────── v1.12.1 Installed Revise ─────────────────────── v3.14.2 Installed QuadGK ─────────────────────── v2.11.3 Installed NaNMath ────────────────────── v1.1.3 Installed libva_jll ──────────────────── v2.23.0+0 Installed Ogg_jll ────────────────────── v1.3.6+0 Installed LoweredCodeUtils ───────────── v3.5.1 Installed Xorg_libxcb_jll ────────────── v1.17.1+0 Installed Unzip ──────────────────────── v0.2.0 Installed Reexport ───────────────────── v1.2.2 Installed GSL_jll ────────────────────── v2.8.1+0 Installed Xorg_xcb_util_wm_jll ───────── v0.4.2+0 Installed Missings ───────────────────── v1.2.0 Installed Preferences ────────────────── v1.5.2 Installed FFMPEG ─────────────────────── v0.4.5 Installed LERC_jll ───────────────────── v4.1.0+0 Installed TableTraits ────────────────── v1.0.1 Installed LaTeXStrings ───────────────── v1.4.0 Installed Crayons ────────────────────── v4.1.1 Installed SortingAlgorithms ──────────── v1.2.2 Installed LLVMOpenMP_jll ─────────────── v18.1.8+0 Installed EpollShim_jll ──────────────── v0.0.20230411+1 (no artifacts on this platform) Installed eudev_jll ──────────────────── v3.2.14+0 Installed Format ─────────────────────── v1.3.7 Installed libdrm_jll ─────────────────── v2.4.125+1 Installed Xorg_libICE_jll ────────────── v1.1.2+0 Installed Grisu ──────────────────────── v1.0.2 Installed DataStructures ─────────────── v0.18.22 Installed Xorg_xcb_util_jll ──────────── v0.4.1+0 Installed DelimitedFiles ─────────────── v1.9.1 Installed UnicodeFun ─────────────────── v0.4.1 Installed URIs ───────────────────────── v1.6.1 Installed FillArrays ─────────────────── v1.16.0 Installed StatsBase ──────────────────── v0.33.21 Installed MacroTools ─────────────────── v0.5.16 Installed x264_jll ───────────────────── v10164.0.1+0 Installed CodecZlib ──────────────────── v0.7.8 Installed mtdev_jll ──────────────────── v1.1.7+0 Installed DataValueInterfaces ────────── v1.0.0 Installed Xorg_libXau_jll ────────────── v1.0.13+0 Installed Xorg_libXfixes_jll ─────────── v6.0.2+0 Installed Qt6ShaderTools_jll ─────────── v6.10.2+1 Installed Libffi_jll ─────────────────── v3.4.7+0 Installed LoggingExtras ──────────────── v1.2.0 Installed Pixman_jll ─────────────────── v0.46.4+0 Installed NamedDims ──────────────────── v1.2.3 Installed StructUtils ────────────────── v2.8.1 Installed libdecor_jll ───────────────── v0.2.2+0 Installed ColorVectorSpace ───────────── v0.11.0 Installed Contour ────────────────────── v0.6.3 Installed Xorg_xcb_util_keysyms_jll ──── v0.4.1+0 Installed Expat_jll ──────────────────── v2.8.0+0 Installed BitFlags ───────────────────── v0.1.9 Installed IrrationalConstants ────────── v0.2.6 Installed libpng_jll ─────────────────── v1.6.58+0 Installed Xorg_libXcursor_jll ────────── v1.2.4+0 Installed libfdk_aac_jll ─────────────── v2.0.4+0 Installed StableRNGs ─────────────────── v1.0.4 Installed HTTP ───────────────────────── v1.11.0 Installed Scratch ────────────────────── v1.3.0 Installed SimpleBufferStream ─────────── v1.2.0 Installed PooledArrays ───────────────── v1.4.3 Installed WorkerUtilities ────────────── v1.6.1 Installed DocStringExtensions ────────── v0.9.5 Installed Qt6Svg_jll ─────────────────── v6.10.2+0 Installed FFMPEG_jll ─────────────────── v8.1.0+0 Installed Qt6Declarative_jll ─────────── v6.10.2+1 Installed PDMats ─────────────────────── v0.11.35 Installed Rmath_jll ──────────────────── v0.5.1+0 Installed RelocatableFolders ─────────── v1.0.1 Installed AliasTables ────────────────── v1.1.3 Installed Ghostscript_jll ────────────── v9.55.1+0 Installed ConcurrentUtilities ────────── v2.5.1 Installed IteratorInterfaceExtensions ── v1.0.0 Installed Plots ──────────────────────── v1.41.6 Installed JLFzf ──────────────────────── v0.1.11 Installed CSV ────────────────────────── v0.10.16 Installed RecipesBase ────────────────── v1.3.4 Installed DataAPI ────────────────────── v1.16.0 Installed InvertedIndices ────────────── v1.3.1 Installed GSL ────────────────────────── v1.0.1 Installed libevdev_jll ───────────────── v1.13.4+0 Installed Xorg_libXi_jll ─────────────── v1.8.3+0 Installed JWAS ───────────────────────── v2.3.6 Installed Wayland_jll ────────────────── v1.24.0+0 Installed ColorSchemes ───────────────── v3.31.0 Installed Measures ───────────────────── v0.3.3 Installed Colors ─────────────────────── v0.13.1 Installed CodeTracking ───────────────── v3.0.2 Installed Xorg_libxkbfile_jll ────────── v1.2.0+0 Installed Libglvnd_jll ───────────────── v1.7.1+1 Installed Glib_jll ───────────────────── v2.86.3+0 Installed Xorg_xkbcomp_jll ───────────── v1.4.7+0 Installed Latexify ───────────────────── v0.16.10 Installed ColorTypes ─────────────────── v0.12.1 Installed Requires ───────────────────── v1.3.1 Installed StringManipulation ─────────── v0.4.4 Installed LogExpFunctions ────────────── v0.3.29 Installed Vulkan_Loader_jll ──────────── v1.3.243+0 Installed Xorg_xcb_util_cursor_jll ───── v0.1.6+0 Installed Parsers ────────────────────── v2.8.4 Installed Xorg_xcb_util_renderutil_jll ─ v0.3.10+0 Installed JuliaInterpreter ───────────── v0.10.12 Installed PrettyTables ───────────────── v3.3.2 Installed JSON ───────────────────────── v1.5.2 Installed Rmath ──────────────────────── v0.9.0 Installed Distributions ──────────────── v0.25.125 Installed StatsFuns ──────────────────── v1.5.2 Installed Xorg_libXdmcp_jll ──────────── v1.1.6+0 Installed x265_jll ───────────────────── v4.1.0+0 Installed Xorg_xkeyboard_config_jll ──── v2.47.0+0 Installed Xorg_libSM_jll ─────────────── v1.2.6+0 Installed XZ_jll ─────────────────────── v5.8.3+0 Installed Pango_jll ──────────────────── v1.57.1+0 Installed WeakRefStrings ─────────────── v1.4.3 Installed JLLWrappers ────────────────── v1.8.0 Installed RecipesPipeline ────────────── v0.6.12 Installed DataFrames ─────────────────── v1.8.2 Installed TranscodingStreams ─────────── v0.11.3 Installed ExceptionUnwrapping ────────── v0.1.11 Installed Xorg_xtrans_jll ────────────── v1.6.0+0 Installed JpegTurbo_jll ──────────────── v3.1.5+0 Installed GR_jll ─────────────────────── v0.73.24+0 Installed Libmount_jll ───────────────── v2.42.0+0 Installed Dbus_jll ───────────────────── v1.16.2+0 Installed HypergeometricFunctions ────── v0.3.28 Installed FilePathsBase ──────────────── v0.9.24 Installed Compat ─────────────────────── v4.18.1 Installed TensorCore ─────────────────── v0.1.1 Installed StatsAPI ───────────────────── v1.8.0 Installed Statistics ─────────────────── v1.11.1 Installed Xorg_libX11_jll ────────────── v1.8.13+0 Installed GR ─────────────────────────── v0.73.24 Installed MbedTLS ────────────────────── v1.1.10 Installed Xorg_xcb_util_image_jll ────── v0.4.1+0 Installed PlotUtils ──────────────────── v1.4.4 Installed xkbcommon_jll ──────────────── v1.13.0+0 Installed OpenSSL ────────────────────── v1.6.1 Installed GettextRuntime_jll ─────────── v0.22.4+0 Installed ProgressMeter ──────────────── v1.11.0 Installed PrecompileTools ────────────── v1.3.3 Installed LAME_jll ───────────────────── v3.100.3+0 Installed libvorbis_jll ──────────────── v1.3.8+0 Installed FixedPointNumbers ──────────── v0.8.5 Installed FriBidi_jll ────────────────── v1.0.17+0 Installed Libiconv_jll ───────────────── v1.18.0+0 Installed Showoff ────────────────────── v1.0.3 Installed GLFW_jll ───────────────────── v3.4.1+1 Installed Xorg_libXrender_jll ────────── v0.9.12+0 Installed PtrArrays ──────────────────── v1.4.0 Installed SpecialFunctions ───────────── v2.7.2 Installed FreeType2_jll ──────────────── v2.14.3+1 Installed MbedTLS_jll ────────────────── v2.28.1010+0 Installed Xorg_libXrandr_jll ─────────── v1.5.6+0 Installed Qt6Wayland_jll ─────────────── v6.10.2+1 Installed Xorg_libXext_jll ───────────── v1.3.8+0 Installed Kronecker ──────────────────── v0.5.5 Installing 82 artifacts Installed artifact Xorg_libXau 36.6 KiB Installed artifact Xorg_xcb_util_keysyms 17.8 KiB Installed artifact x265 1.4 MiB Installed artifact libdrm 368.0 KiB Installed artifact Dbus 489.8 KiB Installed artifact x264 2.1 MiB Installed artifact Xorg_libICE 384.9 KiB Installed artifact Xorg_xcb_util_wm 147.9 KiB Installed artifact libevdev 117.1 KiB Installed artifact Ogg 250.3 KiB Installed artifact LLVMOpenMP 661.6 KiB Installed artifact Xorg_libXdmcp 67.7 KiB Installed artifact Xorg_xkeyboard_config 545.0 KiB Installed artifact libpng 329.4 KiB Installed artifact OpenSpecFun 194.9 KiB Installed artifact Libtiff 2.3 MiB Installed artifact GSL 4.6 MiB Installed artifact Xorg_libXcursor 227.1 KiB Installed artifact Wayland 390.6 KiB Installed artifact Xorg_xkbcomp 274.8 KiB Installed artifact Xorg_libXrender 435.9 KiB Installed artifact Xorg_xcb_util_image 54.3 KiB Installed artifact libaom 6.4 MiB Installed artifact Fontconfig 984.8 KiB Installed artifact libinput 1.0 MiB Installed artifact Xorg_xtrans 48.2 KiB Installed artifact fzf 3.3 MiB Installed artifact GR 19.0 MiB Installed artifact Xorg_libX11 4.8 MiB Installed artifact Xorg_libXext 286.6 KiB Installed artifact FreeType2 1.5 MiB Installed artifact Xorg_xcb_util 39.5 KiB Installed artifact MbedTLS 2.2 MiB Installed artifact LERC 267.4 KiB Installed artifact FriBidi 78.9 KiB Installed artifact libvorbis 271.0 KiB Installed artifact Xorg_libXfixes 59.0 KiB Installed artifact Xorg_xcb_util_cursor 27.6 KiB Installed artifact Xorg_libxkbfile 91.1 KiB Installed artifact Xorg_libXrandr 97.3 KiB Installed artifact Cairo 2.2 MiB Installed artifact HarfBuzz 1.7 MiB Installed artifact Libffi 44.2 KiB Installed artifact xkbcommon 298.7 KiB Installed artifact eudev 3.9 MiB Installed artifact Bzip2 503.5 KiB Installed artifact Libmount 6.9 MiB Installed artifact Opus 960.9 KiB Installed artifact Qt6Svg 386.4 KiB Installed artifact LAME 292.5 KiB Installed artifact Ghostscript 31.4 MiB Installed artifact Xorg_libXi 1.6 MiB Installed artifact mtdev 79.6 KiB Installed artifact Qt6Wayland 1.4 MiB Installed artifact Glib 7.7 MiB Installed artifact libdecor 44.1 KiB Installed artifact libfdk_aac 2.8 MiB Installed artifact FFMPEG 11.9 MiB Installed artifact libva 235.7 KiB Installed artifact Libiconv 1.9 MiB Installed artifact Vulkan_Loader 177.3 KiB Installed artifact XZ 1.6 MiB Installed artifact Qt6ShaderTools 2.1 MiB Installed artifact libass 427.8 KiB Installed artifact Pixman 390.8 KiB Installed artifact Expat 287.9 KiB Installed artifact Xorg_libxcb 2.1 MiB Installed artifact Libuuid 3.9 MiB Installed artifact GLFW 187.9 KiB Installed artifact Rmath 121.9 KiB Installed artifact JpegTurbo 1.4 MiB Installed artifact Xorg_xcb_util_renderutil 32.1 KiB Installed artifact Xorg_libXinerama 25.5 KiB Installed artifact Xorg_libSM 164.1 KiB Installed artifact GettextRuntime 543.0 KiB Installed artifact Pango 2.1 MiB Installed artifact Graphite2 120.2 KiB Installed artifact Xorg_libpciaccess 26.2 KiB Installed artifact Libglvnd 833.5 KiB Installed artifact testfiles 6.2 MiB Installed artifact Qt6Declarative 23.8 MiB Installed artifact Qt6Base 23.1 MiB Updating `~/.julia/environments/v1.14/Project.toml` [c9a035f4] + JWAS v2.3.6 Updating `~/.julia/environments/v1.14/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [d1d4a3ce] + BitFlags v0.1.9 [336ed68f] + CSV v0.10.16 [da1fd8a2] + CodeTracking v3.0.2 [944b1d66] + CodecZlib v0.7.8 [35d6a980] + ColorSchemes v3.31.0 [3da002f7] + ColorTypes v0.12.1 [c3611d14] + ColorVectorSpace v0.11.0 [5ae59095] + Colors v0.13.1 [34da2185] + Compat v4.18.1 [807dbc54] + Compiler v0.1.1 [f0e56b4a] + ConcurrentUtilities v2.5.1 [d38c429a] + Contour v0.6.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.8.2 ⌅ [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [31c24e10] + Distributions v0.25.125 [ffbed154] + DocStringExtensions v0.9.5 [460bff9d] + ExceptionUnwrapping v0.1.11 [c87230d0] + FFMPEG v0.4.5 [48062228] + FilePathsBase v0.9.24 [1a297f60] + FillArrays v1.16.0 [53c48c17] + FixedPointNumbers v0.8.5 [1fa38f19] + Format v1.3.7 [28b8d3ca] + GR v0.73.24 [92c85e6c] + GSL v1.0.1 [42e2da0e] + Grisu v1.0.2 [cd3eb016] + HTTP v1.11.0 [34004b35] + HypergeometricFunctions v0.3.28 [842dd82b] + InlineStrings v1.4.5 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [1019f520] + JLFzf v0.1.11 [692b3bcd] + JLLWrappers v1.8.0 [682c06a0] + JSON v1.5.2 [c9a035f4] + JWAS v2.3.6 [aa1ae85d] + JuliaInterpreter v0.10.12 [2c470bb0] + Kronecker v0.5.5 [b964fa9f] + LaTeXStrings v1.4.0 [23fbe1c1] + Latexify v0.16.10 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [6f1432cf] + LoweredCodeUtils v3.5.1 [1914dd2f] + MacroTools v0.5.16 [739be429] + MbedTLS v1.1.10 [442fdcdd] + Measures v0.3.3 [e1d29d7a] + Missings v1.2.0 [77ba4419] + NaNMath v1.1.3 [356022a1] + NamedDims v1.2.3 [4d8831e6] + OpenSSL v1.6.1 [bac558e1] + OrderedCollections v1.8.1 ⌃ [90014a1f] + PDMats v0.11.35 [69de0a69] + Parsers v2.8.4 [ccf2f8ad] + PlotThemes v3.3.0 [995b91a9] + PlotUtils v1.4.4 [91a5bcdd] + Plots v1.41.6 [2dfb63ee] + PooledArrays v1.4.3 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.2 [08abe8d2] + PrettyTables v3.3.2 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.4.0 [1fd47b50] + QuadGK v2.11.3 [3cdcf5f2] + RecipesBase v1.3.4 [01d81517] + RecipesPipeline v0.6.12 [189a3867] + Reexport v1.2.2 [05181044] + RelocatableFolders v1.0.1 [ae029012] + Requires v1.3.1 [295af30f] + Revise v3.14.2 [79098fc4] + Rmath v0.9.0 [6c6a2e73] + Scratch v1.3.0 [91c51154] + SentinelArrays v1.4.9 [992d4aef] + Showoff v1.0.3 [777ac1f9] + SimpleBufferStream v1.2.0 [a2af1166] + SortingAlgorithms v1.2.2 [276daf66] + SpecialFunctions v2.7.2 [860ef19b] + StableRNGs v1.0.4 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 ⌅ [2913bbd2] + StatsBase v0.33.21 [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.4.4 [ec057cc2] + StructUtils v2.8.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [62fd8b95] + TensorCore v0.1.1 [3bb67fe8] + TranscodingStreams v0.11.3 [5c2747f8] + URIs v1.6.1 [1cfade01] + UnicodeFun v0.4.1 [41fe7b60] + Unzip v0.2.0 [ea10d353] + WeakRefStrings v1.4.3 [76eceee3] + WorkerUtilities v1.6.1 [6e34b625] + Bzip2_jll v1.0.9+0 [83423d85] + Cairo_jll v1.18.7+0 [ee1fde0b] + Dbus_jll v1.16.2+0 [2702e6a9] + EpollShim_jll v0.0.20230411+1 [2e619515] + Expat_jll v2.8.0+0 [b22a6f82] + FFMPEG_jll v8.1.0+0 [a3f928ae] + Fontconfig_jll v2.17.1+0 [d7e528f0] + FreeType2_jll v2.14.3+1 [559328eb] + FriBidi_jll v1.0.17+0 [0656b61e] + GLFW_jll v3.4.1+1 [d2c73de3] + GR_jll v0.73.24+0 [1b77fbbe] + GSL_jll v2.8.1+0 ⌅ [b0724c58] + GettextRuntime_jll v0.22.4+0 [61579ee1] + Ghostscript_jll v9.55.1+0 [7746bdde] + Glib_jll v2.86.3+0 [3b182d85] + Graphite2_jll v1.3.15+0 [2e76f6c2] + HarfBuzz_jll v8.5.1+0 [aacddb02] + JpegTurbo_jll v3.1.5+0 [c1c5ebd0] + LAME_jll v3.100.3+0 [88015f11] + LERC_jll v4.1.0+0 [1d63c593] + LLVMOpenMP_jll v18.1.8+0 ⌅ [e9f186c6] + Libffi_jll v3.4.7+0 [7e76a0d4] + Libglvnd_jll v1.7.1+1 [94ce4f54] + Libiconv_jll v1.18.0+0 [4b2f31a3] + Libmount_jll v2.42.0+0 [89763e89] + Libtiff_jll v4.7.2+0 [38a345b3] + Libuuid_jll v2.42.0+0 [c8ffd9c3] + MbedTLS_jll v2.28.1010+0 [e7412a2a] + Ogg_jll v1.3.6+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [91d4177d] + Opus_jll v1.6.1+0 [36c8627f] + Pango_jll v1.57.1+0 [30392449] + Pixman_jll v0.46.4+0 [c0090381] + Qt6Base_jll v6.10.2+1 [629bc702] + Qt6Declarative_jll v6.10.2+1 [ce943373] + Qt6ShaderTools_jll v6.10.2+1 [6de9746b] + Qt6Svg_jll v6.10.2+0 [e99dba38] + Qt6Wayland_jll v6.10.2+1 [f50d1b31] + Rmath_jll v0.5.1+0 [a44049a8] + Vulkan_Loader_jll v1.3.243+0 [a2964d1f] + Wayland_jll v1.24.0+0 [ffd25f8a] + XZ_jll v5.8.3+0 [f67eecfb] + Xorg_libICE_jll v1.1.2+0 [c834827a] + Xorg_libSM_jll v1.2.6+0 [4f6342f7] + Xorg_libX11_jll v1.8.13+0 [0c0b7dd1] + Xorg_libXau_jll v1.0.13+0 [935fb764] + Xorg_libXcursor_jll v1.2.4+0 [a3789734] + Xorg_libXdmcp_jll v1.1.6+0 [1082639a] + Xorg_libXext_jll v1.3.8+0 [d091e8ba] + Xorg_libXfixes_jll v6.0.2+0 [a51aa0fd] + Xorg_libXi_jll v1.8.3+0 [d1454406] + Xorg_libXinerama_jll v1.1.7+0 [ec84b674] + Xorg_libXrandr_jll v1.5.6+0 [ea2f1a96] + Xorg_libXrender_jll v0.9.12+0 [a65dc6b1] + Xorg_libpciaccess_jll v0.19.0+0 [c7cfdc94] + Xorg_libxcb_jll v1.17.1+0 [cc61e674] + Xorg_libxkbfile_jll v1.2.0+0 [e920d4aa] + Xorg_xcb_util_cursor_jll v0.1.6+0 [12413925] + Xorg_xcb_util_image_jll v0.4.1+0 [2def613f] + Xorg_xcb_util_jll v0.4.1+0 [975044d2] + Xorg_xcb_util_keysyms_jll v0.4.1+0 [0d47668e] + Xorg_xcb_util_renderutil_jll v0.3.10+0 [c22f9ab0] + Xorg_xcb_util_wm_jll v0.4.2+0 [35661453] + Xorg_xkbcomp_jll v1.4.7+0 [33bec58e] + Xorg_xkeyboard_config_jll v2.47.0+0 [c5fb5394] + Xorg_xtrans_jll v1.6.0+0 [35ca27e7] + eudev_jll v3.2.14+0 [214eeab7] + fzf_jll v0.61.1+0 [a4ae2306] + libaom_jll v3.13.3+0 [0ac62f75] + libass_jll v0.17.4+0 [1183f4f0] + libdecor_jll v0.2.2+0 [8e53e030] + libdrm_jll v2.4.125+1 [2db6ffa8] + libevdev_jll v1.13.4+0 [f638f0a6] + libfdk_aac_jll v2.0.4+0 [36db933b] + libinput_jll v1.28.1+0 [b53b4c65] + libpng_jll v1.6.58+0 [9a156e7d] + libva_jll v2.23.0+0 [f27f6e37] + libvorbis_jll v1.3.8+0 [009596ad] + mtdev_jll v1.1.7+0 ⌅ [1270edf5] + x264_jll v10164.0.1+0 [dfaa095f] + x265_jll v4.1.0+0 [d8fb68d0] + xkbcommon_jll v1.13.0+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.13.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.14.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.0.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.13.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.5.1+0 [deac9b47] + LibCURL_jll v8.19.0+0 [e37daf67] + LibGit2_jll v1.9.3+0 [29816b5a] + LibSSH2_jll v1.11.101+0 [14a3606d] + MozillaCACerts_jll v2026.3.19 [4536629a] + OpenBLAS_jll v0.3.33+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.6+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.2+0 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.69.0+0 [3f19e933] + p7zip_jll v17.8.0+0 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` Installation completed after 31.49s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling project... 4.7 s ✓ TestEnv 1 dependency successfully precompiled in 5 seconds. 27 already precompiled. Precompiling package dependencies... Precompiling project... 4.4 s ✓ MacroTools 1.8 s ✓ InlineStrings 0.9 s ✓ Reexport 1.0 s ✓ TensorCore 2.2 s ✓ IrrationalConstants 1.0 s ✓ Measures 0.9 s ✓ DataValueInterfaces 1.0 s ✓ StatsAPI 2.4 s ✓ Format 1.0 s ✓ Contour 1.5 s ✓ TranscodingStreams 0.9 s ✓ LaTeXStrings 1.2 s ✓ Statistics 1.0 s ✓ StableRNGs 0.9 s ✓ PtrArrays 0.9 s ✓ SimpleBufferStream 1.3 s ✓ Grisu 1.0 s ✓ DataAPI 1.3 s ✓ URIs 1.0 s ✓ InvertedIndices 1.0 s ✓ BitFlags 1.1 s ✓ WorkerUtilities 2.3 s ✓ FillArrays 1.5 s ✓ OrderedCollections 1.2 s ✓ DocStringExtensions 1.2 s ✓ Unzip 1.9 s ✓ Compiler 0.9 s ✓ IteratorInterfaceExtensions 2.1 s ✓ Crayons 1.1 s ✓ DelimitedFiles 1.2 s ✓ NaNMath 1.3 s ✓ Requires 1.6 s ✓ ConcurrentUtilities 2.5 s ✓ ProgressMeter 1.4 s ✓ Scratch 2.6 s ✓ SentinelArrays 1.5 s ✓ LoggingExtras 1.9 s ✓ StructUtils 2.2 s ✓ PDMats 1.3 s ✓ Compat 1.6 s ✓ Preferences 2.0 s ✓ ExceptionUnwrapping 3.8 s ✓ UnicodeFun 3.0 s ✓ CodeTracking 1.0 s ✓ CodecZlib 1.5 s ✓ Statistics → SparseArraysExt 4.2 s ✓ FixedPointNumbers 2.4 s ✓ NamedDims 1.0 s ✓ AliasTables 1.3 s ✓ Showoff 1.1 s ✓ Missings 1.1 s ✓ PooledArrays 4.1 s ✓ OpenSSL 1.7 s ✓ FillArrays → FillArraysSparseArraysExt 1.1 s ✓ FillArrays → FillArraysStatisticsExt 1.4 s ✓ LogExpFunctions 0.8 s ✓ TableTraits 1.5 s ✓ RelocatableFolders 1.7 s ✓ FillArrays → FillArraysPDMatsExt 0.9 s ✓ Compat → CompatLinearAlgebraExt 1.2 s ✓ PrecompileTools 2.1 s ✓ JLLWrappers 11.2 s ✓ JuliaInterpreter 2.7 s ✓ ColorTypes 1.9 s ✓ Tables 2.4 s ✓ FilePathsBase 3.7 s ✓ DataStructures 3.7 s ✓ StringManipulation 3.5 s ✓ RecipesBase 16.1 s ✓ Parsers 2.2 s ✓ GSL_jll 2.4 s ✓ Libffi_jll 2.5 s ✓ Xorg_libICE_jll 2.5 s ✓ eudev_jll 2.4 s ✓ mtdev_jll 2.3 s ✓ Bzip2_jll 2.4 s ✓ XZ_jll 2.4 s ✓ Rmath_jll 2.4 s ✓ OpenSpecFun_jll 1.7 s ✓ Xorg_xtrans_jll 2.3 s ✓ Opus_jll 2.4 s ✓ x265_jll 2.3 s ✓ fzf_jll 2.3 s ✓ libpng_jll 1.6 s ✓ EpollShim_jll 2.3 s ✓ Libmount_jll 2.3 s ✓ libfdk_aac_jll 2.2 s ✓ Libuuid_jll 2.3 s ✓ FriBidi_jll 2.3 s ✓ LERC_jll 2.2 s ✓ Xorg_libXau_jll 2.2 s ✓ Ogg_jll 2.2 s ✓ libevdev_jll 2.2 s ✓ LAME_jll 2.2 s ✓ Graphite2_jll 2.6 s ✓ x264_jll 2.4 s ✓ Xorg_libpciaccess_jll 2.5 s ✓ LLVMOpenMP_jll 2.5 s ✓ Libiconv_jll 2.4 s ✓ MbedTLS_jll 2.3 s ✓ libaom_jll 2.5 s ✓ Xorg_libXdmcp_jll 2.5 s ✓ Expat_jll 2.4 s ✓ JpegTurbo_jll 19.4 s ✓ LoweredCodeUtils 1.4 s ✓ ColorTypes → StyledStringsExt 8.2 s ✓ Colors 4.2 s ✓ ColorVectorSpace 1.3 s ✓ StructUtils → StructUtilsTablesExt 3.2 s ✓ FilePathsBase → FilePathsBaseTestExt 1.5 s ✓ FilePathsBase → FilePathsBaseMmapExt 1.7 s ✓ SortingAlgorithms 2.9 s ✓ QuadGK 41.0 s ✓ PrettyTables 8.0 s ✓ JSON 1.4 s ✓ InlineStrings → ParsersExt 15.4 s ✓ GSL 2.3 s ✓ Xorg_libSM_jll 2.3 s ✓ FreeType2_jll 1.9 s ✓ Rmath 5.5 s ✓ SpecialFunctions 2.7 s ✓ JLFzf 2.3 s ✓ libvorbis_jll 2.2 s ✓ libinput_jll 2.2 s ✓ libdrm_jll 2.2 s ✓ Pixman_jll 2.2 s ✓ GettextRuntime_jll 2.4 s ✓ MbedTLS 2.3 s ✓ Xorg_libxcb_jll 2.2 s ✓ Dbus_jll 2.2 s ✓ Wayland_jll 2.3 s ✓ Ghostscript_jll 2.2 s ✓ Libtiff_jll 12.6 s ✓ Revise 9.4 s ✓ ColorSchemes 5.5 s ✓ StatsBase 80.2 s ✓ DataFrames 3.1 s ✓ WeakRefStrings 2.6 s ✓ Fontconfig_jll 2.9 s ✓ HypergeometricFunctions 1.8 s ✓ ColorVectorSpace → SpecialFunctionsExt 2.5 s ✓ Glib_jll 26.9 s ✓ HTTP 2.8 s ✓ Xorg_xcb_util_jll 2.5 s ✓ Xorg_libX11_jll 7.3 s ✓ Latexify 5.2 s ✓ Revise → DistributedExt 21.8 s ✓ PlotUtils 4.2 s ✓ Kronecker 27.1 s ✓ CSV 3.9 s ✓ StatsFuns 2.7 s ✓ Xorg_xcb_util_renderutil_jll 2.9 s ✓ Xorg_xcb_util_wm_jll 2.7 s ✓ Xorg_xcb_util_image_jll 2.5 s ✓ Xorg_xcb_util_keysyms_jll 2.7 s ✓ Xorg_libXfixes_jll 3.0 s ✓ Xorg_libXrender_jll 2.6 s ✓ Xorg_libXext_jll 2.5 s ✓ Xorg_libxkbfile_jll 3.0 s ✓ Latexify → SparseArraysExt 7.9 s ✓ Latexify → DataFramesExt 10.7 s ✓ PlotThemes 10.2 s ✓ RecipesPipeline 11.4 s ✓ Distributions 2.5 s ✓ Xorg_xcb_util_cursor_jll 2.6 s ✓ Xorg_libXcursor_jll 2.7 s ✓ Xorg_libXrandr_jll 2.4 s ✓ Xorg_libXinerama_jll 2.5 s ✓ Xorg_libXi_jll 2.5 s ✓ Cairo_jll 2.5 s ✓ libva_jll 2.5 s ✓ Libglvnd_jll 2.5 s ✓ Xorg_xkbcomp_jll 4.4 s ✓ Distributions → DistributionsTestExt 2.9 s ✓ HarfBuzz_jll 1.8 s ✓ Xorg_xkeyboard_config_jll 2.6 s ✓ libass_jll 2.5 s ✓ Pango_jll 2.5 s ✓ xkbcommon_jll 2.7 s ✓ FFMPEG_jll 2.5 s ✓ Vulkan_Loader_jll 2.5 s ✓ libdecor_jll 1.5 s ✓ FFMPEG 2.8 s ✓ Qt6Base_jll 2.5 s ✓ GLFW_jll 2.7 s ✓ Qt6ShaderTools_jll 2.5 s ✓ Qt6Svg_jll 2.7 s ✓ GR_jll 2.7 s ✓ Qt6Declarative_jll 2.6 s ✓ Qt6Wayland_jll 7.1 s ✓ GR 107.9 s ✓ Plots 15.2 s ✓ JWAS 193 dependencies successfully precompiled in 857 seconds. 39 already precompiled. Precompilation completed after 877.2s ################################################################################ # Testing # Testing JWAS Status `/tmp/jl_ZBZtKk/Project.toml` [336ed68f] CSV v0.10.16 [a93c6f00] DataFrames v1.8.2 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.125 [92c85e6c] GSL v1.0.1 [cd3eb016] HTTP v1.11.0 [c9a035f4] JWAS v2.3.6 [2c470bb0] Kronecker v0.5.5 [91a5bcdd] Plots v1.41.6 [92933f4c] ProgressMeter v1.11.0 [295af30f] Revise v3.14.2 [10745b16] Statistics v1.11.1 ⌅ [2913bbd2] StatsBase v0.33.21 [b77e0a4c] InteractiveUtils v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [2f01184e] SparseArrays v1.13.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_ZBZtKk/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [d1d4a3ce] BitFlags v0.1.9 [336ed68f] CSV v0.10.16 [da1fd8a2] CodeTracking v3.0.2 [944b1d66] CodecZlib v0.7.8 [35d6a980] ColorSchemes v3.31.0 [3da002f7] ColorTypes v0.12.1 [c3611d14] ColorVectorSpace v0.11.0 [5ae59095] Colors v0.13.1 [34da2185] Compat v4.18.1 [807dbc54] Compiler v0.1.1 [f0e56b4a] ConcurrentUtilities v2.5.1 [d38c429a] Contour v0.6.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.8.2 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.125 [ffbed154] DocStringExtensions v0.9.5 [460bff9d] ExceptionUnwrapping v0.1.11 [c87230d0] FFMPEG v0.4.5 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.16.0 [53c48c17] FixedPointNumbers v0.8.5 [1fa38f19] Format v1.3.7 [28b8d3ca] GR v0.73.24 [92c85e6c] GSL v1.0.1 [42e2da0e] Grisu v1.0.2 [cd3eb016] HTTP v1.11.0 [34004b35] HypergeometricFunctions v0.3.28 [842dd82b] InlineStrings v1.4.5 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [82899510] IteratorInterfaceExtensions v1.0.0 [1019f520] JLFzf v0.1.11 [692b3bcd] JLLWrappers v1.8.0 [682c06a0] JSON v1.5.2 [c9a035f4] JWAS v2.3.6 [aa1ae85d] JuliaInterpreter v0.10.12 [2c470bb0] Kronecker v0.5.5 [b964fa9f] LaTeXStrings v1.4.0 [23fbe1c1] Latexify v0.16.10 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.2.0 [6f1432cf] LoweredCodeUtils v3.5.1 [1914dd2f] MacroTools v0.5.16 [739be429] MbedTLS v1.1.10 [442fdcdd] Measures v0.3.3 [e1d29d7a] Missings v1.2.0 [77ba4419] NaNMath v1.1.3 [356022a1] NamedDims v1.2.3 [4d8831e6] OpenSSL v1.6.1 [bac558e1] OrderedCollections v1.8.1 ⌃ [90014a1f] PDMats v0.11.35 [69de0a69] Parsers v2.8.4 [ccf2f8ad] PlotThemes v3.3.0 [995b91a9] PlotUtils v1.4.4 [91a5bcdd] Plots v1.41.6 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.2 [08abe8d2] PrettyTables v3.3.2 [92933f4c] ProgressMeter v1.11.0 [43287f4e] PtrArrays v1.4.0 [1fd47b50] QuadGK v2.11.3 [3cdcf5f2] RecipesBase v1.3.4 [01d81517] RecipesPipeline v0.6.12 [189a3867] Reexport v1.2.2 [05181044] RelocatableFolders v1.0.1 [ae029012] Requires v1.3.1 [295af30f] Revise v3.14.2 [79098fc4] Rmath v0.9.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.9 [992d4aef] Showoff v1.0.3 [777ac1f9] SimpleBufferStream v1.2.0 [a2af1166] SortingAlgorithms v1.2.2 [276daf66] SpecialFunctions v2.7.2 [860ef19b] StableRNGs v1.0.4 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.8.0 ⌅ [2913bbd2] StatsBase v0.33.21 [4c63d2b9] StatsFuns v1.5.2 [892a3eda] StringManipulation v0.4.4 [ec057cc2] StructUtils v2.8.1 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [62fd8b95] TensorCore v0.1.1 [3bb67fe8] TranscodingStreams v0.11.3 [5c2747f8] URIs v1.6.1 [1cfade01] UnicodeFun v0.4.1 [41fe7b60] Unzip v0.2.0 [ea10d353] WeakRefStrings v1.4.3 [76eceee3] WorkerUtilities v1.6.1 [6e34b625] Bzip2_jll v1.0.9+0 [83423d85] Cairo_jll v1.18.7+0 [ee1fde0b] Dbus_jll v1.16.2+0 [2702e6a9] EpollShim_jll v0.0.20230411+1 [2e619515] Expat_jll v2.8.0+0 [b22a6f82] FFMPEG_jll v8.1.0+0 [a3f928ae] Fontconfig_jll v2.17.1+0 [d7e528f0] FreeType2_jll v2.14.3+1 [559328eb] FriBidi_jll v1.0.17+0 [0656b61e] GLFW_jll v3.4.1+1 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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... ====================================================================== JWAS.jl Comprehensive Test Suite ====================================================================== Cleaning up old test artifacts... Cleanup complete. ====================================================================== Running tests in: test_run_12463 ====================================================================== Test Configuration: Unit Tests: Always run ✓ Integration Tests: Disabled (set RUN_INTEGRATION_TESTS=true) ====================================================================== ┌ Warning: Estimated marker memory usage exceeds configured guard threshold. │ context: test │ estimated: 600.00 B │ threshold (50.0% of RAM): 500.00 B │ system RAM: 1000.00 B │ Set memory_guard=:warn or :off to override, or reduce model/data size. └ @ JWAS ~/.julia/packages/JWAS/1xzHb/src/1.JWAS/src/markers/tools4genotypes.jl:231 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder guardrail_error_mode is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file guardrail_error_mode/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder guardrail_off_mode is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file guardrail_off_mode/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 10 burnin 0 starting_value true printout_frequency 11 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file guardrail_off_mode/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file guardrail_off_mode/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file guardrail_off_mode/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file guardrail_off_mode/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file guardrail_off_mode/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file guardrail_off_mode/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file guardrail_off_mode/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 20%|███████ | ETA: 0:00:16 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:09 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Streaming genotype files are created with prefix /tmp/jl_hiF6hK/geno_missing_stream. The delimiter in geno_missing.csv is ','. The header (marker IDs) is provided in geno_missing.csv. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 4; #individuals: 6 Genotype informatin: #markers: 4; #individuals: 6 (storage=:stream) Streaming genotype files are created with prefix /tmp/jl_hiF6hK/geno_nomissing_stream. The delimiter in geno_nomissing.csv is ','. The header (marker IDs) is provided in geno_nomissing.csv. Genotype informatin: #markers: 4; #individuals: 6 The folder dense_results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 6 observations are used in the analysis.These individual IDs are saved in the file dense_results/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.503496 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 40 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.503 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file dense_results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file dense_results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file dense_results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file dense_results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Genotype informatin: #markers: 4; #individuals: 6 (storage=:stream) The folder stream_results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 6 observations are used in the analysis.These individual IDs are saved in the file stream_results/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.503496 storage=:stream is enabled; genotype alignment is skipped and original ID order is used. A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 40 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.503 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file stream_results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file stream_results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file stream_results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file stream_results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. running MCMC ... 5%|█▊ | ETA: 0:00:25 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:01 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Streaming genotype files are created with prefix /tmp/jl_ODnTqa/geno_stream. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder stream_fast_blocks is created to save results. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder stream_weighted is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file stream_weighted/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.680851 storage=:stream is enabled; genotype alignment is skipped and original ID order is used. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder stream_mt is created to save results. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder stream_double is created to save results. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Streaming genotype files are created with prefix /tmp/jl_BoCqrR/geno_stream. The delimiter in geno.csv is ','. The header (marker IDs) is provided in geno.csv. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 6 Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) Streaming genotype files are created with prefix /tmp/jl_BoCqrR/geno_uncentered_stream. The argument center=true is ignored for storage=:stream. Backend metadata centered=false is used. Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) Streaming genotype files are created with prefix /tmp/jl_BoCqrR/cleanup_true_stream. Streaming genotype files are created with prefix /tmp/jl_BoCqrR/cleanup_false_stream. Auto conversion mode selected :dense (estimated dense bytes=120, auto_dense_max_bytes=10000). Streaming genotype files are created with prefix /tmp/jl_BoCqrR/auto_dense_stream. Auto conversion mode selected :lowmem (estimated dense bytes=120, auto_dense_max_bytes=1). Streaming genotype files are created with prefix /tmp/jl_BoCqrR/auto_lowmem_stream. Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Missing values (9.0) are replaced by column means. 1 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 2; #individuals: 4 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. 1 loci which are fixed or have minor allele frequency < 0.01 are removed. Streaming genotype files are created with prefix /tmp/jl_bEnPw7/annotated_stream_qc_stream. Genotype informatin: #markers: 2; #individuals: 4 (storage=:stream) 1 loci which are fixed or have minor allele frequency < 0.01 are removed. Streaming genotype files are created with prefix /tmp/jl_FksUcv/annotated_stream_legacy_stream. The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 20; #individuals: 2 The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 20; #individuals: 2 The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 20; #individuals: 2 The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 20; #individuals: 2 The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 1; #individuals: 2 The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 3; #individuals: 3 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_LCfqrEFcdG is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file /tmp/jl_LCfqrEFcdG/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_singletrait_sampler_auto Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file /tmp/jl_LCfqrEFcdG/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file /tmp/jl_LCfqrEFcdG/MCMC_samples_marker_effects_annotated_singletrait_sampler_auto_y1.txt is created to save MCMC samples for marker_effects_annotated_singletrait_sampler_auto_y1. The file /tmp/jl_LCfqrEFcdG/MCMC_samples_marker_effects_variances_annotated_singletrait_sampler_auto.txt is created to save MCMC samples for marker_effects_variances_annotated_singletrait_sampler_auto. The file /tmp/jl_LCfqrEFcdG/MCMC_samples_pi_annotated_singletrait_sampler_auto.txt is created to save MCMC samples for pi_annotated_singletrait_sampler_auto. running MCMC ... 20%|███████ | ETA: 0:00:02 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:00 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_JWfaTA5uj8 is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_annotated_bayesc_fastblocks is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 7 observations are used in the analysis.These individual IDs are saved in the file test_mt_annotated_bayesc_fastblocks/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_mt_fastblocks y2 = intercept + annotated_mt_fastblocks Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file test_mt_annotated_bayesc_fastblocks/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_annotated_bayesc_fastblocks/MCMC_samples_marker_effects_annotated_mt_fastblocks_y1.txt is created to save MCMC samples for marker_effects_annotated_mt_fastblocks_y1. The file test_mt_annotated_bayesc_fastblocks/MCMC_samples_marker_effects_annotated_mt_fastblocks_y2.txt is created to save MCMC samples for marker_effects_annotated_mt_fastblocks_y2. The file test_mt_annotated_bayesc_fastblocks/MCMC_samples_marker_effects_variances_annotated_mt_fastblocks.txt is created to save MCMC samples for marker_effects_variances_annotated_mt_fastblocks. The file test_mt_annotated_bayesc_fastblocks/MCMC_samples_pi_annotated_mt_fastblocks.txt is created to save MCMC samples for pi_annotated_mt_fastblocks. running MCMC ... 40%|██████████████ | ETA: 0:01:03 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:45 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Streaming genotype files are created with prefix /tmp/jl_sEiLQn/annotated_mt_stream_stream. Genotype informatin: #markers: 5; #individuals: 4 (storage=:stream) The folder /tmp/jl_rOL8hKZ8XY is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file results/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + plain_geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 12 burnin 2 starting_value true printout_frequency 99 output_samples_frequency 1 constraint on residual variance false constraint on marker effect variance for plain_geno false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category plain_geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_plain_geno_y1.txt is created to save MCMC samples for marker_effects_plain_geno_y1. The file results/MCMC_samples_marker_effects_variances_plain_geno.txt is created to save MCMC samples for marker_effects_variances_plain_geno. The file results/MCMC_samples_pi_plain_geno.txt is created to save MCMC samples for pi_plain_geno. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file results/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 30 burnin 10 starting_value true printout_frequency 31 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_geno false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π_j (min/mean/max) 0.000 / 0.000 / 0.000 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_geno_y1.txt is created to save MCMC samples for marker_effects_annotated_geno_y1. The file results/MCMC_samples_marker_effects_variances_annotated_geno.txt is created to save MCMC samples for marker_effects_variances_annotated_geno. The file results/MCMC_samples_pi_annotated_geno.txt is created to save MCMC samples for pi_annotated_geno. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file results/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_blocks Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 15 burnin 10 starting_value true printout_frequency 31 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_blocks false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_blocks Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π_j (min/mean/max) 0.000 / 0.000 / 0.000 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_blocks_y1.txt is created to save MCMC samples for marker_effects_annotated_blocks_y1. The file results/MCMC_samples_marker_effects_variances_annotated_blocks.txt is created to save MCMC samples for marker_effects_variances_annotated_blocks. The file results/MCMC_samples_pi_annotated_blocks.txt is created to save MCMC samples for pi_annotated_blocks. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 13%|████▋ | ETA: 0:00:43 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:06 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file results/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 BLOCK STARTS: [1, 3, 5] A Linear Mixed Model was build using model equations: y1 = intercept + annotated_blocks_independent Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 12 burnin 2 starting_value true printout_frequency 13 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_blocks_independent false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_blocks_independent Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π_j (min/mean/max) 0.000 / 0.000 / 0.000 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_blocks_independent_y1.txt is created to save MCMC samples for marker_effects_annotated_blocks_independent_y1. The file results/MCMC_samples_marker_effects_variances_annotated_blocks_independent.txt is created to save MCMC samples for marker_effects_variances_annotated_blocks_independent. The file results/MCMC_samples_pi_annotated_blocks_independent.txt is created to save MCMC samples for pi_annotated_blocks_independent. running MCMC ... 17%|█████▉ | ETA: 0:00:13 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:02 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Streaming genotype files are created with prefix /tmp/jl_6vnLG4/annotated_stream_stream. Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) The folder results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 6 observations are used in the analysis.These individual IDs are saved in the file results/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.402235 storage=:stream is enabled; genotype alignment is skipped and original ID order is used. A Linear Mixed Model was build using model equations: y1 = intercept + annotated_stream Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 30 burnin 10 starting_value true printout_frequency 31 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_stream false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_stream Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.402 π_j (min/mean/max) 0.000 / 0.000 / 0.000 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_stream_y1.txt is created to save MCMC samples for marker_effects_annotated_stream_y1. The file results/MCMC_samples_marker_effects_variances_annotated_stream.txt is created to save MCMC samples for marker_effects_variances_annotated_stream. The file results/MCMC_samples_pi_annotated_stream.txt is created to save MCMC samples for pi_annotated_stream. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 Streaming genotype files are created with prefix /tmp/jl_hMhdrQ/annotated_bayesr_stream_stream. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_TaLulILOnI is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_dmrkRqfefy is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file results/IDs_for_individuals_with_phenotypes.txt. The prior for the BayesR shared marker variance is calculated from the genetic variance and π. The mean of the prior for the shared marker variance is: 72.420929 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_bayesr_dense Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 30 burnin 10 starting_value true printout_frequency 31 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_bayesr_dense false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_bayesr_dense Method BayesR genetic variances (genomic): 1.000 marker effect variances: 72.421 π_j (min/mean/max) 0.005 / 0.250 / 0.950 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 BayesR gamma: [0.0, 0.01, 0.1, 1.0] BayesR starting pi: [0.95, 0.03, 0.015, 0.005] BayesR expected class counts: [4.75, 0.15, 0.08, 0.02] The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_bayesr_dense_y1.txt is created to save MCMC samples for marker_effects_annotated_bayesr_dense_y1. The file results/MCMC_samples_marker_effects_variances_annotated_bayesr_dense.txt is created to save MCMC samples for marker_effects_variances_annotated_bayesr_dense. The file results/MCMC_samples_pi_annotated_bayesr_dense.txt is created to save MCMC samples for pi_annotated_bayesr_dense. running MCMC ... 7%|██▍ | ETA: 0:00:26 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:02 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file results/IDs_for_individuals_with_phenotypes.txt. The prior for the BayesR shared marker variance is calculated from the genetic variance and π. The mean of the prior for the shared marker variance is: 72.420929 BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_bayesr_fast Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 15 burnin 10 starting_value true printout_frequency 31 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_bayesr_fast false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_bayesr_fast Method BayesR genetic variances (genomic): 1.000 marker effect variances: 72.421 π_j (min/mean/max) 0.005 / 0.250 / 0.950 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 BayesR gamma: [0.0, 0.01, 0.1, 1.0] BayesR starting pi: [0.95, 0.03, 0.015, 0.005] BayesR expected class counts: [4.75, 0.15, 0.08, 0.02] The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_bayesr_fast_y1.txt is created to save MCMC samples for marker_effects_annotated_bayesr_fast_y1. The file results/MCMC_samples_marker_effects_variances_annotated_bayesr_fast.txt is created to save MCMC samples for marker_effects_variances_annotated_bayesr_fast. The file results/MCMC_samples_pi_annotated_bayesr_fast.txt is created to save MCMC samples for pi_annotated_bayesr_fast. running MCMC ... 13%|████▋ | ETA: 0:00:30 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:04 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file results/IDs_for_individuals_with_phenotypes.txt. The prior for the BayesR shared marker variance is calculated from the genetic variance and π. The mean of the prior for the shared marker variance is: 72.420929 BLOCK STARTS: [1, 3, 5] A Linear Mixed Model was build using model equations: y1 = intercept + annotated_bayesr_fast_independent Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 12 burnin 2 starting_value true printout_frequency 13 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_bayesr_fast_independent false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_bayesr_fast_independent Method BayesR genetic variances (genomic): 1.000 marker effect variances: 72.421 π_j (min/mean/max) 0.005 / 0.250 / 0.950 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 BayesR gamma: [0.0, 0.01, 0.1, 1.0] BayesR starting pi: [0.95, 0.03, 0.015, 0.005] BayesR expected class counts: [4.75, 0.15, 0.08, 0.02] The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_bayesr_fast_independent_y1.txt is created to save MCMC samples for marker_effects_annotated_bayesr_fast_independent_y1. The file results/MCMC_samples_marker_effects_variances_annotated_bayesr_fast_independent.txt is created to save MCMC samples for marker_effects_variances_annotated_bayesr_fast_independent. The file results/MCMC_samples_pi_annotated_bayesr_fast_independent.txt is created to save MCMC samples for pi_annotated_bayesr_fast_independent. running MCMC ... 17%|█████▉ | ETA: 0:00:16 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:03 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in pedigree.txt is ','. coding pedigree... 17%|█████▍ | ETA: 0:00:01 coding pedigree... 100%|████████████████████████████████| Time: 0:00:00 calculating inbreeding... 17%|████▏ | ETA: 0:00:02 calculating inbreeding... 100%|█████████████████████████| Time: 0:00:00 Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The folder test_set_random_ped is created to save results. Checking pedigree... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_set_random_ped/IDs_for_individuals_with_phenotypes.txt. A Linear Mixed Model was build using model equations: y1 = intercept + ID Model Information: Term C/F F/R nLevels intercept factor fixed 1 ID factor random 12 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 1 constraint on residual variance false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: random effect variances (y1:ID): [1.600000023841858;;] genetic variances (polygenic): 1.6f0 residual variances: 1.000 Genomic Information: Degree of freedom for hyper-parameters: residual variances: 4.000 polygenic effect variances: 5.000 The file test_set_random_ped/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_set_random_ped/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file test_set_random_ped/MCMC_samples_y1.ID_variances.txt is created to save MCMC samples for y1:ID_variances. The file test_set_random_ped/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_set_random_ped/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_set_random_ped/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 4%|█▍ | ETA: 0:01:27 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:03 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_ebv_output is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_ebv_output/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_ebv_output/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_ebv_output/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_ebv_output/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_ebv_output/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_ebv_output/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_ebv_output/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_ebv_output/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_ebv_h2 is created to save results. RR-BLUP runs with estimatePi = false. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_ebv_h2/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method RR-BLUP genetic variances (genomic): 1.000 marker effect variances: 0.492 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_ebv_h2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_ebv_h2/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_ebv_h2/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_ebv_h2/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_ebv_h2/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_ebv_h2/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_ebv_h2/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:00:26 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:00 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mcmc_samples is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mcmc_samples/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_mcmc_samples/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mcmc_samples/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mcmc_samples/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mcmc_samples/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mcmc_samples/MCMC_samples_y1.intercept.txt is created to save MCMC samples for y1:intercept. The file test_mcmc_samples/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mcmc_samples/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mcmc_samples/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 Get individual IDs, inverse of numerator relationship matrix, and inbreeding coefficients. Pedigree information: #individuals: 3 #sires: 1 #dams: 1 #founders: 2 The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_invalid_method is created to save results. The folder test_ss_no_geno is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_freq_zero is created to save results. A Linear Mixed Model was build using model equations: y1 = intercept + x1 Model Information: Term C/F F/R nLevels intercept factor fixed 0 x1 factor fixed 0 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. A genomic relationship matrix is computed from genotypes. Genotype informatin: #markers: 0; #individuals: 7 The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_single_step_inline_ids is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_single_step_inline_ids/IDs_for_individuals_with_phenotypes.txt. Prior information for genomic variance is not provided and is generated from the data. Prior information for residual variance is not provided and is generated from the data. calculating A inverse 0.678042 seconds (138.07 k allocations: 8.024 MiB, 99.92% compilation time) imputing missing genotypes for genotypes 8.735600 seconds (2.39 M allocations: 142.929 MiB, 11.09% gc time, 88.80% compilation time) completed imputing genotypes for genotypes The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 1.234511 A Linear Mixed Model was build using model equations: y1 = intercept + genotypes Model Information: Term C/F F/R nLevels intercept factor fixed 1 ϵ factor random 5 J covariate fixed 1 The file test_single_step_inline_ids/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_single_step_inline_ids/MCMC_samples_marker_effects_genotypes_y1.txt is created to save MCMC samples for marker_effects_genotypes_y1. The file test_single_step_inline_ids/MCMC_samples_marker_effects_variances_genotypes.txt is created to save MCMC samples for marker_effects_variances_genotypes. The file test_single_step_inline_ids/MCMC_samples_pi_genotypes.txt is created to save MCMC samples for pi_genotypes. The file test_single_step_inline_ids/MCMC_samples_y1.J.txt is created to save MCMC samples for y1:J. The file test_single_step_inline_ids/MCMC_samples_y1.ϵ.txt is created to save MCMC samples for y1:ϵ. The file test_single_step_inline_ids/MCMC_samples_y1.ϵ_variances.txt is created to save MCMC samples for y1:ϵ_variances. The file test_single_step_inline_ids/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_single_step_multiclass is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_single_step_multiclass/IDs_for_individuals_with_phenotypes.txt. Prior information for genomic variance is not provided and is generated from the data. Prior information for genomic variance is not provided and is generated from the data. Prior information for residual variance is not provided and is generated from the data. calculating A inverse 0.000139 seconds (173 allocations: 11.656 KiB) imputing missing genotypes for geno1 0.950045 seconds (528 allocations: 36.070 KiB, 99.92% gc time) completed imputing genotypes for geno1 imputing missing genotypes for geno2 0.905556 seconds (524 allocations: 36.008 KiB, 99.94% gc time) completed imputing genotypes for geno2 The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.617255 The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.617255 A Linear Mixed Model was build using model equations: y1 = intercept + geno1 + geno2 Model Information: Term C/F F/R nLevels intercept factor fixed 1 ϵ factor random 5 J covariate fixed 1 The file test_single_step_multiclass/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_single_step_multiclass/MCMC_samples_marker_effects_geno1_y1.txt is created to save MCMC samples for marker_effects_geno1_y1. The file test_single_step_multiclass/MCMC_samples_marker_effects_variances_geno1.txt is created to save MCMC samples for marker_effects_variances_geno1. The file test_single_step_multiclass/MCMC_samples_pi_geno1.txt is created to save MCMC samples for pi_geno1. The file test_single_step_multiclass/MCMC_samples_marker_effects_geno2_y1.txt is created to save MCMC samples for marker_effects_geno2_y1. The file test_single_step_multiclass/MCMC_samples_marker_effects_variances_geno2.txt is created to save MCMC samples for marker_effects_variances_geno2. The file test_single_step_multiclass/MCMC_samples_pi_geno2.txt is created to save MCMC samples for pi_geno2. The file test_single_step_multiclass/MCMC_samples_y1.J.txt is created to save MCMC samples for y1:J. The file test_single_step_multiclass/MCMC_samples_y1.ϵ.txt is created to save MCMC samples for y1:ϵ. The file test_single_step_multiclass/MCMC_samples_y1.ϵ_variances.txt is created to save MCMC samples for y1:ϵ_variances. The file test_single_step_multiclass/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_bayesc is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_bayesc/IDs_for_individuals_with_phenotypes.txt. Pi (Π) is not provided. Pi (Π) is generated assuming all markers have effects on all traits. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 0.492462 0.246231 0.246231 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesc/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesc/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_bayesc/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_bayesc/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_bayesc/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_bayesc/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_bayesc/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_bayesc/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_bayesc/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:11:52 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:15 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_annotated_bayesc is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 7 observations are used in the analysis.These individual IDs are saved in the file test_mt_annotated_bayesc/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 1.231156 1.231156 1.231156 1.407035 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_mt_geno y2 = intercept + annotated_mt_geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 40 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_mt_geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_mt_geno Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 1.231 1.231 1.231 1.407 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.15 ["N", "N"] 0.45 ["Y", "Y"] 0.2 ["Y", "N"] 0.2 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_annotated_bayesc/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_annotated_bayesc/MCMC_samples_marker_effects_annotated_mt_geno_y1.txt is created to save MCMC samples for marker_effects_annotated_mt_geno_y1. The file test_mt_annotated_bayesc/MCMC_samples_marker_effects_annotated_mt_geno_y2.txt is created to save MCMC samples for marker_effects_annotated_mt_geno_y2. The file test_mt_annotated_bayesc/MCMC_samples_marker_effects_variances_annotated_mt_geno.txt is created to save MCMC samples for marker_effects_variances_annotated_mt_geno. The file test_mt_annotated_bayesc/MCMC_samples_pi_annotated_mt_geno.txt is created to save MCMC samples for pi_annotated_mt_geno. The file test_mt_annotated_bayesc/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_annotated_bayesc/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_annotated_bayesc/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_annotated_bayesc/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 5%|█▊ | ETA: 0:03:10 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:10 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_annotated_bayesc_sampler2 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 7 observations are used in the analysis.These individual IDs are saved in the file test_mt_annotated_bayesc_sampler2/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 1.231156 1.231156 1.231156 1.407035 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_mt_geno_sampler2 y2 = intercept + annotated_mt_geno_sampler2 Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 40 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_mt_geno_sampler2 false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_mt_geno_sampler2 Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 1.231 1.231 1.231 1.407 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.15 ["N", "N"] 0.45 ["Y", "Y"] 0.2 ["Y", "N"] 0.2 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_annotated_bayesc_sampler2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_annotated_bayesc_sampler2/MCMC_samples_marker_effects_annotated_mt_geno_sampler2_y1.txt is created to save MCMC samples for marker_effects_annotated_mt_geno_sampler2_y1. The file test_mt_annotated_bayesc_sampler2/MCMC_samples_marker_effects_annotated_mt_geno_sampler2_y2.txt is created to save MCMC samples for marker_effects_annotated_mt_geno_sampler2_y2. The file test_mt_annotated_bayesc_sampler2/MCMC_samples_marker_effects_variances_annotated_mt_geno_sampler2.txt is created to save MCMC samples for marker_effects_variances_annotated_mt_geno_sampler2. The file test_mt_annotated_bayesc_sampler2/MCMC_samples_pi_annotated_mt_geno_sampler2.txt is created to save MCMC samples for pi_annotated_mt_geno_sampler2. The file test_mt_annotated_bayesc_sampler2/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_annotated_bayesc_sampler2/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_annotated_bayesc_sampler2/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_annotated_bayesc_sampler2/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 5%|█▊ | ETA: 0:02:40 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:08 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_bayesc_dense_sampler2 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_bayesc_dense_sampler2/IDs_for_individuals_with_phenotypes.txt. Pi (Π) is not provided. Pi (Π) is generated assuming all markers have effects on all traits. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 0.492462 0.246231 0.246231 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + plain_mt_dense_sampler2 y2 = intercept + plain_mt_dense_sampler2 Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 40 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for plain_mt_dense_sampler2 false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category plain_mt_dense_sampler2 Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesc_dense_sampler2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesc_dense_sampler2/MCMC_samples_marker_effects_plain_mt_dense_sampler2_y1.txt is created to save MCMC samples for marker_effects_plain_mt_dense_sampler2_y1. The file test_mt_bayesc_dense_sampler2/MCMC_samples_marker_effects_plain_mt_dense_sampler2_y2.txt is created to save MCMC samples for marker_effects_plain_mt_dense_sampler2_y2. The file test_mt_bayesc_dense_sampler2/MCMC_samples_marker_effects_variances_plain_mt_dense_sampler2.txt is created to save MCMC samples for marker_effects_variances_plain_mt_dense_sampler2. The file test_mt_bayesc_dense_sampler2/MCMC_samples_pi_plain_mt_dense_sampler2.txt is created to save MCMC samples for pi_plain_mt_dense_sampler2. The file test_mt_bayesc_dense_sampler2/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_bayesc_dense_sampler2/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_bayesc_dense_sampler2/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_bayesc_dense_sampler2/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 5%|█▊ | ETA: 0:02:20 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:07 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_bayesc_fastblocks is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_bayesc_fastblocks/IDs_for_individuals_with_phenotypes.txt. Pi (Π) is not provided. Pi (Π) is generated assuming all markers have effects on all traits. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 0.492462 0.246231 0.246231 0.492462 BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + plain_mt_fastblocks y2 = intercept + plain_mt_fastblocks Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 20 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for plain_mt_fastblocks false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category plain_mt_fastblocks Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesc_fastblocks/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesc_fastblocks/MCMC_samples_marker_effects_plain_mt_fastblocks_y1.txt is created to save MCMC samples for marker_effects_plain_mt_fastblocks_y1. The file test_mt_bayesc_fastblocks/MCMC_samples_marker_effects_plain_mt_fastblocks_y2.txt is created to save MCMC samples for marker_effects_plain_mt_fastblocks_y2. The file test_mt_bayesc_fastblocks/MCMC_samples_marker_effects_variances_plain_mt_fastblocks.txt is created to save MCMC samples for marker_effects_variances_plain_mt_fastblocks. The file test_mt_bayesc_fastblocks/MCMC_samples_pi_plain_mt_fastblocks.txt is created to save MCMC samples for pi_plain_mt_fastblocks. The file test_mt_bayesc_fastblocks/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_bayesc_fastblocks/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_bayesc_fastblocks/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_bayesc_fastblocks/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 10%|███▌ | ETA: 0:02:32 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:16 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_bayesc_fastblocks_sampler2 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_bayesc_fastblocks_sampler2/IDs_for_individuals_with_phenotypes.txt. Pi (Π) is not provided. Pi (Π) is generated assuming all markers have effects on all traits. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 0.492462 0.246231 0.246231 0.492462 BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + plain_mt_fastblocks_sampler2 y2 = intercept + plain_mt_fastblocks_sampler2 Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 20 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for plain_mt_fastblocks_sampler2 false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category plain_mt_fastblocks_sampler2 Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_marker_effects_plain_mt_fastblocks_sampler2_y1.txt is created to save MCMC samples for marker_effects_plain_mt_fastblocks_sampler2_y1. The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_marker_effects_plain_mt_fastblocks_sampler2_y2.txt is created to save MCMC samples for marker_effects_plain_mt_fastblocks_sampler2_y2. The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_marker_effects_variances_plain_mt_fastblocks_sampler2.txt is created to save MCMC samples for marker_effects_variances_plain_mt_fastblocks_sampler2. The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_pi_plain_mt_fastblocks_sampler2.txt is created to save MCMC samples for pi_plain_mt_fastblocks_sampler2. The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_bayesc_fastblocks_sampler2/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 10%|███▌ | ETA: 0:02:10 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:14 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_annotated_bayesc_fastblocks_sampler2 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 7 observations are used in the analysis.These individual IDs are saved in the file test_mt_annotated_bayesc_fastblocks_sampler2/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 1.231156 1.231156 1.231156 1.407035 BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_mt_fastblocks_sampler2 y2 = intercept + annotated_mt_fastblocks_sampler2 Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 20 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_mt_fastblocks_sampler2 false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_mt_fastblocks_sampler2 Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 1.231 1.231 1.231 1.407 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.15 ["N", "N"] 0.45 ["Y", "Y"] 0.2 ["Y", "N"] 0.2 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_marker_effects_annotated_mt_fastblocks_sampler2_y1.txt is created to save MCMC samples for marker_effects_annotated_mt_fastblocks_sampler2_y1. The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_marker_effects_annotated_mt_fastblocks_sampler2_y2.txt is created to save MCMC samples for marker_effects_annotated_mt_fastblocks_sampler2_y2. The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_marker_effects_variances_annotated_mt_fastblocks_sampler2.txt is created to save MCMC samples for marker_effects_variances_annotated_mt_fastblocks_sampler2. The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_pi_annotated_mt_fastblocks_sampler2.txt is created to save MCMC samples for pi_annotated_mt_fastblocks_sampler2. The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_annotated_bayesc_fastblocks_sampler2/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 10%|███▌ | ETA: 0:01:48 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:12 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_bayesc_independent_sampler1 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_bayesc_independent_sampler1/IDs_for_individuals_with_phenotypes.txt. Pi (Π) is not provided. Pi (Π) is generated assuming all markers have effects on all traits. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 0.492462 0.246231 0.246231 0.492462 BLOCK STARTS: [1, 3, 5] A Linear Mixed Model was build using model equations: y1 = intercept + plain_mt_independent_sampler1 y2 = intercept + plain_mt_independent_sampler1 Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 12 burnin 2 starting_value true printout_frequency 13 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for plain_mt_independent_sampler1 false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category plain_mt_independent_sampler1 Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesc_independent_sampler1/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesc_independent_sampler1/MCMC_samples_marker_effects_plain_mt_independent_sampler1_y1.txt is created to save MCMC samples for marker_effects_plain_mt_independent_sampler1_y1. The file test_mt_bayesc_independent_sampler1/MCMC_samples_marker_effects_plain_mt_independent_sampler1_y2.txt is created to save MCMC samples for marker_effects_plain_mt_independent_sampler1_y2. The file test_mt_bayesc_independent_sampler1/MCMC_samples_marker_effects_variances_plain_mt_independent_sampler1.txt is created to save MCMC samples for marker_effects_variances_plain_mt_independent_sampler1. The file test_mt_bayesc_independent_sampler1/MCMC_samples_pi_plain_mt_independent_sampler1.txt is created to save MCMC samples for pi_plain_mt_independent_sampler1. running MCMC ... 17%|█████▉ | ETA: 0:01:32 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:18 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_bayesc_independent_sampler2 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_bayesc_independent_sampler2/IDs_for_individuals_with_phenotypes.txt. Pi (Π) is not provided. Pi (Π) is generated assuming all markers have effects on all traits. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 0.492462 0.246231 0.246231 0.492462 BLOCK STARTS: [1, 3, 5] A Linear Mixed Model was build using model equations: y1 = intercept + plain_mt_independent_sampler2 y2 = intercept + plain_mt_independent_sampler2 Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 12 burnin 2 starting_value true printout_frequency 13 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for plain_mt_independent_sampler2 false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category plain_mt_independent_sampler2 Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesc_independent_sampler2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesc_independent_sampler2/MCMC_samples_marker_effects_plain_mt_independent_sampler2_y1.txt is created to save MCMC samples for marker_effects_plain_mt_independent_sampler2_y1. The file test_mt_bayesc_independent_sampler2/MCMC_samples_marker_effects_plain_mt_independent_sampler2_y2.txt is created to save MCMC samples for marker_effects_plain_mt_independent_sampler2_y2. The file test_mt_bayesc_independent_sampler2/MCMC_samples_marker_effects_variances_plain_mt_independent_sampler2.txt is created to save MCMC samples for marker_effects_variances_plain_mt_independent_sampler2. The file test_mt_bayesc_independent_sampler2/MCMC_samples_pi_plain_mt_independent_sampler2.txt is created to save MCMC samples for pi_plain_mt_independent_sampler2. running MCMC ... 17%|█████▉ | ETA: 0:01:12 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:14 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_annotated_bayesc_independent_sampler1 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 7 observations are used in the analysis.These individual IDs are saved in the file test_mt_annotated_bayesc_independent_sampler1/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 1.231156 1.231156 1.231156 1.407035 BLOCK STARTS: [1, 3, 5] A Linear Mixed Model was build using model equations: y1 = intercept + annotated_mt_independent_sampler1 y2 = intercept + annotated_mt_independent_sampler1 Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 12 burnin 2 starting_value true printout_frequency 13 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_mt_independent_sampler1 false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_mt_independent_sampler1 Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 1.231 1.231 1.231 1.407 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.15 ["N", "N"] 0.45 ["Y", "Y"] 0.2 ["Y", "N"] 0.2 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_annotated_bayesc_independent_sampler1/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_annotated_bayesc_independent_sampler1/MCMC_samples_marker_effects_annotated_mt_independent_sampler1_y1.txt is created to save MCMC samples for marker_effects_annotated_mt_independent_sampler1_y1. The file test_mt_annotated_bayesc_independent_sampler1/MCMC_samples_marker_effects_annotated_mt_independent_sampler1_y2.txt is created to save MCMC samples for marker_effects_annotated_mt_independent_sampler1_y2. The file test_mt_annotated_bayesc_independent_sampler1/MCMC_samples_marker_effects_variances_annotated_mt_independent_sampler1.txt is created to save MCMC samples for marker_effects_variances_annotated_mt_independent_sampler1. The file test_mt_annotated_bayesc_independent_sampler1/MCMC_samples_pi_annotated_mt_independent_sampler1.txt is created to save MCMC samples for pi_annotated_mt_independent_sampler1. running MCMC ... 17%|█████▉ | ETA: 0:01:25 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:17 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_annotated_bayesc_independent_sampler2 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 7 observations are used in the analysis.These individual IDs are saved in the file test_mt_annotated_bayesc_independent_sampler2/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 1.231156 1.231156 1.231156 1.407035 BLOCK STARTS: [1, 3, 5] A Linear Mixed Model was build using model equations: y1 = intercept + annotated_mt_independent_sampler2 y2 = intercept + annotated_mt_independent_sampler2 Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 12 burnin 2 starting_value true printout_frequency 13 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_mt_independent_sampler2 false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_mt_independent_sampler2 Method BayesC genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 1.231 1.231 1.231 1.407 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.15 ["N", "N"] 0.45 ["Y", "Y"] 0.2 ["Y", "N"] 0.2 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_annotated_bayesc_independent_sampler2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_annotated_bayesc_independent_sampler2/MCMC_samples_marker_effects_annotated_mt_independent_sampler2_y1.txt is created to save MCMC samples for marker_effects_annotated_mt_independent_sampler2_y1. The file test_mt_annotated_bayesc_independent_sampler2/MCMC_samples_marker_effects_annotated_mt_independent_sampler2_y2.txt is created to save MCMC samples for marker_effects_annotated_mt_independent_sampler2_y2. The file test_mt_annotated_bayesc_independent_sampler2/MCMC_samples_marker_effects_variances_annotated_mt_independent_sampler2.txt is created to save MCMC samples for marker_effects_variances_annotated_mt_independent_sampler2. The file test_mt_annotated_bayesc_independent_sampler2/MCMC_samples_pi_annotated_mt_independent_sampler2.txt is created to save MCMC samples for pi_annotated_mt_independent_sampler2. running MCMC ... 17%|█████▉ | ETA: 0:01:06 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:13 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_rrblup is created to save results. RR-BLUP runs with estimatePi = false. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_rrblup/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 0.492462 0.246231 0.246231 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method RR-BLUP genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 0.492 0.246 0.246 0.492 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_rrblup/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_rrblup/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_rrblup/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_rrblup/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_rrblup/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_rrblup/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_rrblup/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_rrblup/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_rrblup/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:03:02 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:03 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The folder test_mt_ped is created to save results. Checking pedigree... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y2:ID"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_ped/IDs_for_individuals_with_phenotypes.txt. A Linear Mixed Model was build using model equations: y1 = intercept + ID y2 = intercept + ID Model Information: Term C/F F/R nLevels intercept factor fixed 1 ID factor random 12 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: random effect variances (y1:ID,y2:ID): 1.0f0 0.5f0 0.5f0 1.0f0 genetic variances (polygenic): 1.0f0 0.5f0 0.5f0 1.0f0 residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: Degree of freedom for hyper-parameters: residual variances: 6.000 polygenic effect variances: 6.000 The file test_mt_ped/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_ped/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file test_mt_ped/MCMC_samples_y1.ID_y2.ID_variances.txt is created to save MCMC samples for y1:ID_y2:ID_variances. The file test_mt_ped/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_ped/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_ped/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_ped/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_mt_iid is created to save results. RR-BLUP runs with estimatePi = false. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_mt_iid/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π. The mean of the prior for the marker effects covariance matrix is: 0.492462 0.246231 0.246231 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + x2 + geno y2 = intercept + x2 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 x2 factor random 2 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: random effect variances (y1:x2,y2:x2): 0.5f0 0.1f0 0.1f0 0.5f0 residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method RR-BLUP genetic variances (genomic): 1.0 0.5 0.5 1.0 marker effect variances: 0.492 0.246 0.246 0.492 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 random effect variances: 6.000 marker effect variances: 6.000 The file test_mt_iid/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_iid/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_iid/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_iid/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_iid/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_iid/MCMC_samples_y1.x2_y2.x2_variances.txt is created to save MCMC samples for y1:x2_y2:x2_variances. The file test_mt_iid/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_iid/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_iid/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_iid/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_bayesb is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_bayesb/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesB genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_bayesb/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_bayesb/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_bayesb/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_bayesb/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_bayesb/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_bayesb/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_bayesb/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_bayesa is created to save results. BayesA runs with estimatePi = false. BayesA is equivalent to BayesB with known π=0. BayesB with known π=0 runs. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_bayesa/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesB genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi false estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_bayesa/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_bayesa/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_bayesa/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_bayesa/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_bayesa/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_bayesa/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_bayesa/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder test_bayesl is created to save results. BayesL runs with estimatePi = false. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_bayesl/IDs_for_individuals_with_phenotypes.txt. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesL genetic variances (genomic): 1.000 marker effect variances: 0.492 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_bayesl/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_bayesl/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_bayesl/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_bayesl/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_bayesl/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_bayesl/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_bayesl/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:02:32 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:03 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. A genomic relationship matrix is computed from genotypes. Genotype informatin: #markers: 0; #individuals: 7 The folder test_gblup is created to save results. GBLUP runs with estimatePi = false. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file test_gblup/IDs_for_individuals_with_phenotypes.txt. GBLUP: Error During Test at /home/pkgeval/.julia/packages/JWAS/1xzHb/test/unit/test_bayesb_methods.jl:56 Got exception outside of a @test MethodError: no method matching Union{Matrix{Float32}, Matrix{Float64}}(::SparseMatrixCSC{Float32, Int64}) The type `Union{Matrix{Float32}, Matrix{Float64}}` exists, but no method is defined for this combination of argument types when trying to construct it. Stacktrace: [1] convert(::Type{Union{Matrix{Float32}, Matrix{Float64}}}, a::SparseMatrixCSC{Float32, Int64}) @ Base ./array.jl:650 [2] setproperty!(x::JWAS.Genotypes, f::Symbol, v::SparseMatrixCSC{Float32, Int64}) @ Base ./Base_compiler.jl:61 [3] align_genotypes(mme::JWAS.MME, output_heritability::Bool, single_step_analysis::Bool) @ JWAS ~/.julia/packages/JWAS/1xzHb/src/1.JWAS/src/markers/tools4genotypes.jl:317 [4] runMCMC(mme::JWAS.MME, df::DataFrame; heterogeneous_residuals::Bool, chain_length::Int64, starting_value::Bool, burnin::Int64, output_samples_frequency::Int64, update_priors_frequency::Int64, single_step_analysis::Bool, pedigree::Bool, fitting_J_vector::Bool, causal_structure::Bool, missing_phenotypes::Bool, RRM::Bool, outputEBV::Bool, output_heritability::Bool, prediction_equation::Bool, seed::Int64, printout_model_info::Bool, printout_frequency::Int64, big_memory::Bool, double_precision::Bool, fast_blocks::Bool, independent_blocks::Bool, memory_guard::Symbol, memory_guard_ratio::Float64, output_folder::String, output_samples_for_all_parameters::Bool, methods::String, Pi::Float64, estimatePi::Bool, estimate_scale::Bool, estimate_variance::Bool, categorical_trait::Bool, censored_trait::Bool) @ JWAS ~/.julia/packages/JWAS/1xzHb/src/1.JWAS/src/JWAS.jl:400 [5] top-level scope @ ~/.julia/packages/JWAS/1xzHb/test/unit/test_bayesb_methods.jl:57 [6] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [7] macro expansion @ ~/.julia/packages/JWAS/1xzHb/test/unit/test_bayesb_methods.jl:60 [inlined] [8] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:327 [9] macro expansion @ ~/.julia/packages/JWAS/1xzHb/test/runtests.jl:128 [inlined] [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [11] macro expansion @ ~/.julia/packages/JWAS/1xzHb/test/runtests.jl:128 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [13] macro expansion @ ~/.julia/packages/JWAS/1xzHb/test/runtests.jl:71 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [15] top-level scope @ ~/.julia/packages/JWAS/1xzHb/test/runtests.jl:70 [16] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:327 [17] top-level scope @ none:6 [18] eval(m::Module, e::Any) @ Core ./boot.jl:517 [19] exec_options(opts::Base.JLOptions) @ Base ./client.jl:318 [20] _start() @ Base ./client.jl:593 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_FEvUteHIFe is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_70VCHKroTU is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file /tmp/jl_70VCHKroTU/IDs_for_individuals_with_phenotypes.txt. The prior for the BayesR shared marker variance is calculated from the genetic variance and π. The mean of the prior for the shared marker variance is: 72.420929 BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file /tmp/jl_70VCHKroTU/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file /tmp/jl_70VCHKroTU/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file /tmp/jl_70VCHKroTU/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file /tmp/jl_70VCHKroTU/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. running MCMC ... 40%|██████████████ | ETA: 0:00:06 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:03 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_503ovgV2FY is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file /tmp/jl_503ovgV2FY/IDs_for_individuals_with_phenotypes.txt. The prior for the BayesR shared marker variance is calculated from the genetic variance and π. The mean of the prior for the shared marker variance is: 72.420929 BLOCK SIZE: 1 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file /tmp/jl_503ovgV2FY/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file /tmp/jl_503ovgV2FY/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file /tmp/jl_503ovgV2FY/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file /tmp/jl_503ovgV2FY/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_rYLYp4d8Ts is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file /tmp/jl_rYLYp4d8Ts/IDs_for_individuals_with_phenotypes.txt. The prior for the BayesR shared marker variance is calculated from the genetic variance and π. The mean of the prior for the shared marker variance is: 72.420929 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file /tmp/jl_rYLYp4d8Ts/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file /tmp/jl_rYLYp4d8Ts/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file /tmp/jl_rYLYp4d8Ts/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file /tmp/jl_rYLYp4d8Ts/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. running MCMC ... 20%|███████ | ETA: 0:00:05 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:01 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Streaming genotype files are created with prefix /tmp/jl_nhZzeW/geno_stream. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder /tmp/jl_RylIL010h0 is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_zKDOH2pKf0 is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_ZabgpGNyNG is created to save results. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_Tor8D2Xa1y is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file /tmp/jl_Tor8D2Xa1y/IDs_for_individuals_with_phenotypes.txt. The prior for the BayesR shared marker variance is calculated from the genetic variance and π. The mean of the prior for the shared marker variance is: 72.420929 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file /tmp/jl_Tor8D2Xa1y/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file /tmp/jl_Tor8D2Xa1y/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file /tmp/jl_Tor8D2Xa1y/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file /tmp/jl_Tor8D2Xa1y/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder /tmp/jl_DOZeNAXUt0 is created to save results. Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any[] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file /tmp/jl_DOZeNAXUt0/IDs_for_individuals_with_phenotypes.txt. The prior for the BayesR shared marker variance is calculated from the genetic variance and π. The mean of the prior for the shared marker variance is: 72.420929 BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file /tmp/jl_DOZeNAXUt0/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file /tmp/jl_DOZeNAXUt0/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file /tmp/jl_DOZeNAXUt0/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file /tmp/jl_DOZeNAXUt0/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.2168 Commit 2569364ac4* (2026-05-09 12:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-21.1.8 (ORCJIT, znver2) GC: Built with stock GC Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_ZBZtKk The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile. --trace-compile is enabled during profile collection. ====================================================================================== cmd: /opt/julia/bin/julia 861 running 0 of 1 signal (10): User defined signal 1 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457:34 wait at ./task.jl:1246:44 wait_forever at ./task.jl:1168:5 jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4125:23 [inlined] ijl_apply_generic at /source/src/gf.c:4351:12 jl_apply at /source/src/julia.h:2328:12 [inlined] start_task at /source/src/task.c:1275:19 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== ┌ Warning: There were no samples collected in one or more groups. │ This may be due to idle threads, or you may need to run your │ program longer (perhaps by running it multiple times), │ or adjust the delay between samples with `Profile.init()`. └ @ Profile /opt/julia/share/julia/stdlib/v1.14/Profile/src/Profile.jl:1361 Overhead ╎ [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x00007b6f9ef04f10 Total snapshots: 717. Utilization: 0% ╎717 @Base/task.jl:1168 wait_forever() 716╎ 717 @Base/task.jl:1246 wait() ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile. --trace-compile is enabled during profile collection. ====================================================================================== cmd: /opt/julia/bin/julia 1 running 0 of 1 signal (10): User defined signal 1 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457:34 wait at ./task.jl:1246:44 wait_forever at ./task.jl:1168:5 jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4125:23 [inlined] ijl_apply_generic at /source/src/gf.c:4351:12 jl_apply at /source/src/julia.h:2328:12 [inlined] start_task at /source/src/task.c:1275:19 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== ┌ Warning: There were no samples collected in one or more groups. │ This may be due to idle threads, or you may need to run your │ program longer (perhaps by running it multiple times), │ or adjust the delay between samples with `Profile.init()`. └ @ Profile /opt/julia/share/julia/stdlib/v1.14/Profile/src/Profile.jl:1361 Overhead ╎ [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x00007d4482ce7a30 Total snapshots: 456. Utilization: 0% ╎456 @Base/task.jl:1168 wait_forever() 455╎ 456 @Base/task.jl:1246 wait() [861] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/JWAS/1xzHb/test/unit/test_bayesr_parity.jl:314 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457:34 wait at ./task.jl:1246:44 wait_forever at ./task.jl:1168:5 jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4125:23 [inlined] ijl_apply_generic at /source/src/gf.c:4351:12 jl_apply at /source/src/julia.h:2328:12 [inlined] start_task at /source/src/task.c:1275:19 unknown function (ip: (nil)) at (unknown file) Allocations: 288922107 (Pool: 288919180; Big: 2927); GC: 87 [1] signal 15: Terminated in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457:34 wait at ./task.jl:1246:44 wait_forever at ./task.jl:1168:5 jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4125:23 [inlined] ijl_apply_generic at /source/src/gf.c:4351:12 jl_apply at /source/src/julia.h:2328:12 [inlined] start_task at /source/src/task.c:1275:19 unknown function (ip: (nil)) at (unknown file) Allocations: 27239441 (Pool: 27238187; Big: 1254); GC: 36 PkgEval terminated after 2738.51s: test duration exceeded the time limit