Package evaluation to test SDeMo on Julia 1.14.0-DEV.1730 (9c1e1fa299*) started at 2026-02-17T17:46:57.219


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...
  Activating project at `~/.julia/environments/v1.14`

Set-up completed after 12.22s


################################################################################
# Installation
#

Installing SDeMo...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [3e5feb82] + SDeMo v1.7.1
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [66dad0bd] + AliasTables v1.1.3
  [7d9fca2a] + Arpack v0.5.4
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.19.3
  [31c24e10] + Distributions v0.25.123
  [ffbed154] + DocStringExtensions v0.9.5
  [1a297f60] + FillArrays v1.16.0
  [34004b35] + HypergeometricFunctions v0.3.28
  [92d709cd] + IrrationalConstants v0.2.6
  [692b3bcd] + JLLWrappers v1.7.1
  [682c06a0] + JSON v1.4.0
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [e1d29d7a] + Missings v1.2.0
  [6f286f6a] + MultivariateStats v0.10.3
  [bac558e1] + OrderedCollections v1.8.1
  [90014a1f] + PDMats v0.11.37
  [69de0a69] + Parsers v2.8.3
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.1
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [189a3867] + Reexport v1.2.2
  [79098fc4] + Rmath v0.9.0
  [3e5feb82] + SDeMo v1.7.1
  [a2af1166] + SortingAlgorithms v1.2.2
  [276daf66] + SpecialFunctions v2.7.1
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.8.0
  [2913bbd2] + StatsBase v0.34.10
  [4c63d2b9] + StatsFuns v1.5.2
  [ec057cc2] + StructUtils v2.6.3
  [1c621080] + TestItems v1.0.0
⌅ [68821587] + Arpack_jll v3.5.2+0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [56f22d72] + Artifacts v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [56ddb016] + Logging v1.11.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v1.0.0
  [9e88b42a] + Serialization v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [4536629a] + OpenBLAS_jll v0.3.30+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [8e850b90] + libblastrampoline_jll v5.15.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 5.41s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...
Precompiling packages...
   5674.2 ms  ✓ SDeMo
  1 dependency successfully precompiled in 7 seconds. 75 already precompiled.

Precompilation completed after 28.88s


################################################################################
# Testing
#

     Testing SDeMo
      Status `/tmp/jl_25OqnT/Project.toml`
  [3e5feb82] SDeMo v1.7.1
  [10745b16] Statistics v1.11.1
  [f8b46487] TestItemRunner v1.1.4
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_25OqnT/Manifest.toml`
  [66dad0bd] AliasTables v1.1.3
  [7d9fca2a] Arpack v0.5.4
  [9a962f9c] DataAPI v1.16.0
  [864edb3b] DataStructures v0.19.3
  [31c24e10] Distributions v0.25.123
  [ffbed154] DocStringExtensions v0.9.5
  [1a297f60] FillArrays v1.16.0
  [34004b35] HypergeometricFunctions v0.3.28
  [92d709cd] IrrationalConstants v0.2.6
  [692b3bcd] JLLWrappers v1.7.1
  [682c06a0] JSON v1.4.0
  [2ab3a3ac] LogExpFunctions v0.3.29
  [e1d29d7a] Missings v1.2.0
  [6f286f6a] MultivariateStats v0.10.3
  [bac558e1] OrderedCollections v1.8.1
  [90014a1f] PDMats v0.11.37
  [69de0a69] Parsers v2.8.3
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.1
  [43287f4e] PtrArrays v1.3.0
  [1fd47b50] QuadGK v2.11.2
  [189a3867] Reexport v1.2.2
  [79098fc4] Rmath v0.9.0
  [3e5feb82] SDeMo v1.7.1
  [a2af1166] SortingAlgorithms v1.2.2
  [276daf66] SpecialFunctions v2.7.1
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.8.0
  [2913bbd2] StatsBase v0.34.10
  [4c63d2b9] StatsFuns v1.5.2
  [ec057cc2] StructUtils v2.6.3
  [f8b46487] TestItemRunner v1.1.4
  [1c621080] TestItems v1.0.0
⌅ [68821587] Arpack_jll v3.5.2+0
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [f50d1b31] Rmath_jll v0.5.1+0
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching 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
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.14.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v1.0.0
  [9e88b42a] Serialization 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 v1.3.0+1
  [deac9b47] LibCURL_jll v8.18.0+0
  [e37daf67] LibGit2_jll v1.9.2+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.12.2
  [4536629a] OpenBLAS_jll v0.3.30+0
  [05823500] OpenLibm_jll v0.8.7+0
  [458c3c95] OpenSSL_jll v3.5.5+0
  [efcefdf7] PCRE2_jll v10.47.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.1+2
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.15.0+0
  [8e850ede] nghttp2_jll v1.68.0+1
  [3f19e933] p7zip_jll v17.7.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
[ Info: [  0%] LOSS: training ≈ 0.501  validation ≈ 0.4886 ( 98%)
[ Info: [  1%] LOSS: training ≈ 0.4294 validation ≈ 0.4132 ( 96%)
[ Info: [  2%] LOSS: training ≈ 0.3944 validation ≈ 0.3732 ( 95%)
[ Info: [  2%] LOSS: training ≈ 0.3737 validation ≈ 0.3481 ( 93%)
[ Info: [  2%] LOSS: training ≈ 0.3599 validation ≈ 0.3307 ( 92%)
[ Info: [  3%] LOSS: training ≈ 0.35   validation ≈ 0.3178 ( 91%)
[ Info: [  4%] LOSS: training ≈ 0.3425 validation ≈ 0.3077 ( 90%)
[ Info: [  4%] LOSS: training ≈ 0.3365 validation ≈ 0.2996 ( 89%)
[ Info: [  4%] LOSS: training ≈ 0.3316 validation ≈ 0.2929 ( 88%)
[ Info: [  5%] LOSS: training ≈ 0.3275 validation ≈ 0.2872 ( 88%)
[ Info: [  6%] LOSS: training ≈ 0.3239 validation ≈ 0.2823 ( 87%)
[ Info: [  6%] LOSS: training ≈ 0.3208 validation ≈ 0.278  ( 87%)
[ Info: [  6%] LOSS: training ≈ 0.3181 validation ≈ 0.2743 ( 86%)
[ Info: [  7%] LOSS: training ≈ 0.3156 validation ≈ 0.2709 ( 86%)
[ Info: [  8%] LOSS: training ≈ 0.3134 validation ≈ 0.2679 ( 86%)
[ Info: [  8%] LOSS: training ≈ 0.3113 validation ≈ 0.2652 ( 85%)
[ Info: [  8%] LOSS: training ≈ 0.3094 validation ≈ 0.2628 ( 85%)
[ Info: [  9%] LOSS: training ≈ 0.3077 validation ≈ 0.2606 ( 85%)
[ Info: [ 10%] LOSS: training ≈ 0.3061 validation ≈ 0.2585 ( 84%)
[ Info: [ 10%] LOSS: training ≈ 0.3046 validation ≈ 0.2566 ( 84%)
[ Info: [ 10%] LOSS: training ≈ 0.3032 validation ≈ 0.2549 ( 84%)
[ Info: [ 11%] LOSS: training ≈ 0.3018 validation ≈ 0.2533 ( 84%)
[ Info: [ 12%] LOSS: training ≈ 0.3006 validation ≈ 0.2517 ( 84%)
[ Info: [ 12%] LOSS: training ≈ 0.2994 validation ≈ 0.2503 ( 84%)
[ Info: [ 12%] LOSS: training ≈ 0.2983 validation ≈ 0.249  ( 83%)
[ Info: [ 13%] LOSS: training ≈ 0.2972 validation ≈ 0.2478 ( 83%)
[ Info: [ 14%] LOSS: training ≈ 0.2962 validation ≈ 0.2466 ( 83%)
[ Info: [ 14%] LOSS: training ≈ 0.2952 validation ≈ 0.2455 ( 83%)
[ Info: [ 14%] LOSS: training ≈ 0.2943 validation ≈ 0.2445 ( 83%)
[ Info: [ 15%] LOSS: training ≈ 0.2934 validation ≈ 0.2435 ( 83%)
[ Info: [ 16%] LOSS: training ≈ 0.2925 validation ≈ 0.2426 ( 83%)
[ Info: [ 16%] LOSS: training ≈ 0.2917 validation ≈ 0.2417 ( 83%)
[ Info: [ 16%] LOSS: training ≈ 0.2909 validation ≈ 0.2409 ( 83%)
[ Info: [ 17%] LOSS: training ≈ 0.2902 validation ≈ 0.24   ( 83%)
[ Info: [ 18%] LOSS: training ≈ 0.2895 validation ≈ 0.2393 ( 83%)
[ Info: [ 18%] LOSS: training ≈ 0.2888 validation ≈ 0.2386 ( 83%)
[ Info: [ 18%] LOSS: training ≈ 0.2881 validation ≈ 0.2379 ( 83%)
[ Info: [ 19%] LOSS: training ≈ 0.2874 validation ≈ 0.2372 ( 83%)
[ Info: [ 20%] LOSS: training ≈ 0.2868 validation ≈ 0.2365 ( 82%)
[ Info: [ 20%] LOSS: training ≈ 0.2862 validation ≈ 0.2359 ( 82%)
[ Info: [ 20%] LOSS: training ≈ 0.2856 validation ≈ 0.2353 ( 82%)
[ Info: [ 21%] LOSS: training ≈ 0.2851 validation ≈ 0.2348 ( 82%)
[ Info: [ 22%] LOSS: training ≈ 0.2845 validation ≈ 0.2342 ( 82%)
[ Info: [ 22%] LOSS: training ≈ 0.284  validation ≈ 0.2337 ( 82%)
[ Info: [ 22%] LOSS: training ≈ 0.2835 validation ≈ 0.2332 ( 82%)
[ Info: [ 23%] LOSS: training ≈ 0.283  validation ≈ 0.2327 ( 82%)
[ Info: [ 24%] LOSS: training ≈ 0.2826 validation ≈ 0.2322 ( 82%)
[ Info: [ 24%] LOSS: training ≈ 0.2821 validation ≈ 0.2318 ( 82%)
[ Info: [ 24%] LOSS: training ≈ 0.2817 validation ≈ 0.2313 ( 82%)
[ Info: [ 25%] LOSS: training ≈ 0.2812 validation ≈ 0.2309 ( 82%)
[ Info: [ 26%] LOSS: training ≈ 0.2808 validation ≈ 0.2305 ( 82%)
[ Info: [ 26%] LOSS: training ≈ 0.2804 validation ≈ 0.2301 ( 82%)
[ Info: [ 26%] LOSS: training ≈ 0.28   validation ≈ 0.2297 ( 82%)
[ Info: [ 27%] LOSS: training ≈ 0.2796 validation ≈ 0.2293 ( 82%)
[ Info: [ 28%] LOSS: training ≈ 0.2793 validation ≈ 0.2289 ( 82%)
[ Info: [ 28%] LOSS: training ≈ 0.2789 validation ≈ 0.2286 ( 82%)
[ Info: [ 28%] LOSS: training ≈ 0.2786 validation ≈ 0.2282 ( 82%)
[ Info: [ 29%] LOSS: training ≈ 0.2782 validation ≈ 0.2279 ( 82%)
[ Info: [ 30%] LOSS: training ≈ 0.2779 validation ≈ 0.2276 ( 82%)
[ Info: [ 30%] LOSS: training ≈ 0.2776 validation ≈ 0.2272 ( 82%)
[ Info: [ 30%] LOSS: training ≈ 0.2773 validation ≈ 0.2269 ( 82%)
[ Info: [ 31%] LOSS: training ≈ 0.277  validation ≈ 0.2266 ( 82%)
[ Info: [ 32%] LOSS: training ≈ 0.2767 validation ≈ 0.2263 ( 82%)
[ Info: [ 32%] LOSS: training ≈ 0.2764 validation ≈ 0.2261 ( 82%)
[ Info: [ 32%] LOSS: training ≈ 0.2762 validation ≈ 0.2258 ( 82%)
[ Info: [ 33%] LOSS: training ≈ 0.2759 validation ≈ 0.2255 ( 82%)
[ Info: [ 34%] LOSS: training ≈ 0.2756 validation ≈ 0.2252 ( 82%)
[ Info: [ 34%] LOSS: training ≈ 0.2754 validation ≈ 0.225  ( 82%)
[ Info: [ 34%] LOSS: training ≈ 0.2751 validation ≈ 0.2247 ( 82%)
[ Info: [ 35%] LOSS: training ≈ 0.2749 validation ≈ 0.2245 ( 82%)
[ Info: [ 36%] LOSS: training ≈ 0.2747 validation ≈ 0.2242 ( 82%)
[ Info: [ 36%] LOSS: training ≈ 0.2744 validation ≈ 0.224  ( 82%)
[ Info: [ 36%] LOSS: training ≈ 0.2742 validation ≈ 0.2238 ( 82%)
[ Info: [ 37%] LOSS: training ≈ 0.274  validation ≈ 0.2236 ( 82%)
[ Info: [ 38%] LOSS: training ≈ 0.2738 validation ≈ 0.2233 ( 82%)
[ Info: [ 38%] LOSS: training ≈ 0.2736 validation ≈ 0.2231 ( 82%)
[ Info: [ 38%] LOSS: training ≈ 0.2734 validation ≈ 0.2229 ( 82%)
[ Info: [ 39%] LOSS: training ≈ 0.2732 validation ≈ 0.2227 ( 82%)
[ Info: [ 40%] LOSS: training ≈ 0.273  validation ≈ 0.2225 ( 81%)
[ Info: [ 40%] LOSS: training ≈ 0.2729 validation ≈ 0.2223 ( 81%)
[ Info: [ 40%] LOSS: training ≈ 0.2727 validation ≈ 0.2221 ( 81%)
[ Info: [ 41%] LOSS: training ≈ 0.2725 validation ≈ 0.2219 ( 81%)
[ Info: [ 42%] LOSS: training ≈ 0.2723 validation ≈ 0.2218 ( 81%)
[ Info: [ 42%] LOSS: training ≈ 0.2722 validation ≈ 0.2216 ( 81%)
[ Info: [ 42%] LOSS: training ≈ 0.272  validation ≈ 0.2214 ( 81%)
[ Info: [ 43%] LOSS: training ≈ 0.2719 validation ≈ 0.2212 ( 81%)
[ Info: [ 44%] LOSS: training ≈ 0.2717 validation ≈ 0.2211 ( 81%)
[ Info: [ 44%] LOSS: training ≈ 0.2716 validation ≈ 0.2209 ( 81%)
[ Info: [ 44%] LOSS: training ≈ 0.2714 validation ≈ 0.2207 ( 81%)
[ Info: [ 45%] LOSS: training ≈ 0.2713 validation ≈ 0.2206 ( 81%)
[ Info: [ 46%] LOSS: training ≈ 0.2711 validation ≈ 0.2204 ( 81%)
[ Info: [ 46%] LOSS: training ≈ 0.271  validation ≈ 0.2203 ( 81%)
[ Info: [ 46%] LOSS: training ≈ 0.2709 validation ≈ 0.2201 ( 81%)
[ Info: [ 47%] LOSS: training ≈ 0.2708 validation ≈ 0.22   ( 81%)
[ Info: [ 48%] LOSS: training ≈ 0.2706 validation ≈ 0.2198 ( 81%)
[ Info: [ 48%] LOSS: training ≈ 0.2705 validation ≈ 0.2197 ( 81%)
[ Info: [ 48%] LOSS: training ≈ 0.2704 validation ≈ 0.2195 ( 81%)
[ Info: [ 49%] LOSS: training ≈ 0.2703 validation ≈ 0.2194 ( 81%)
[ Info: [ 50%] LOSS: training ≈ 0.2702 validation ≈ 0.2193 ( 81%)
[ Info: [ 50%] LOSS: training ≈ 0.2701 validation ≈ 0.2191 ( 81%)
[ Info: [ 50%] LOSS: training ≈ 0.2699 validation ≈ 0.219  ( 81%)
[ Info: [ 51%] LOSS: training ≈ 0.2698 validation ≈ 0.2189 ( 81%)
[ Info: [ 52%] LOSS: training ≈ 0.2697 validation ≈ 0.2188 ( 81%)
[ Info: [ 52%] LOSS: training ≈ 0.2696 validation ≈ 0.2186 ( 81%)
[ Info: [ 52%] LOSS: training ≈ 0.2695 validation ≈ 0.2185 ( 81%)
[ Info: [ 53%] LOSS: training ≈ 0.2694 validation ≈ 0.2184 ( 81%)
[ Info: [ 54%] LOSS: training ≈ 0.2693 validation ≈ 0.2183 ( 81%)
[ Info: [ 54%] LOSS: training ≈ 0.2693 validation ≈ 0.2182 ( 81%)
[ Info: [ 55%] LOSS: training ≈ 0.2692 validation ≈ 0.218  ( 81%)
[ Info: [ 55%] LOSS: training ≈ 0.2691 validation ≈ 0.2179 ( 81%)
[ Info: [ 56%] LOSS: training ≈ 0.269  validation ≈ 0.2178 ( 81%)
[ Info: [ 56%] LOSS: training ≈ 0.2689 validation ≈ 0.2177 ( 81%)
[ Info: [ 56%] LOSS: training ≈ 0.2688 validation ≈ 0.2176 ( 81%)
[ Info: [ 57%] LOSS: training ≈ 0.2687 validation ≈ 0.2175 ( 81%)
[ Info: [ 57%] LOSS: training ≈ 0.2687 validation ≈ 0.2174 ( 81%)
[ Info: [ 58%] LOSS: training ≈ 0.2686 validation ≈ 0.2173 ( 81%)
[ Info: [ 58%] LOSS: training ≈ 0.2685 validation ≈ 0.2172 ( 81%)
[ Info: [ 59%] LOSS: training ≈ 0.2684 validation ≈ 0.2171 ( 81%)
[ Info: [ 60%] LOSS: training ≈ 0.2684 validation ≈ 0.217  ( 81%)
[ Info: [ 60%] LOSS: training ≈ 0.2683 validation ≈ 0.2169 ( 81%)
[ Info: [ 60%] LOSS: training ≈ 0.2682 validation ≈ 0.2168 ( 81%)
[ Info: [ 61%] LOSS: training ≈ 0.2682 validation ≈ 0.2167 ( 81%)
[ Info: [ 62%] LOSS: training ≈ 0.2681 validation ≈ 0.2167 ( 81%)
[ Info: [ 62%] LOSS: training ≈ 0.268  validation ≈ 0.2166 ( 81%)
[ Info: [ 62%] LOSS: training ≈ 0.268  validation ≈ 0.2165 ( 81%)
[ Info: [ 63%] LOSS: training ≈ 0.2679 validation ≈ 0.2164 ( 81%)
[ Info: [ 64%] LOSS: training ≈ 0.2678 validation ≈ 0.2163 ( 81%)
[ Info: [ 64%] LOSS: training ≈ 0.2678 validation ≈ 0.2162 ( 81%)
[ Info: [ 64%] LOSS: training ≈ 0.2677 validation ≈ 0.2162 ( 81%)
[ Info: [ 65%] LOSS: training ≈ 0.2677 validation ≈ 0.2161 ( 81%)
[ Info: [ 66%] LOSS: training ≈ 0.2676 validation ≈ 0.216  ( 81%)
[ Info: [ 66%] LOSS: training ≈ 0.2676 validation ≈ 0.2159 ( 81%)
[ Info: [ 66%] LOSS: training ≈ 0.2675 validation ≈ 0.2158 ( 81%)
[ Info: [ 67%] LOSS: training ≈ 0.2675 validation ≈ 0.2158 ( 81%)
[ Info: [ 68%] LOSS: training ≈ 0.2674 validation ≈ 0.2157 ( 81%)
[ Info: [ 68%] LOSS: training ≈ 0.2674 validation ≈ 0.2156 ( 81%)
[ Info: [ 68%] LOSS: training ≈ 0.2673 validation ≈ 0.2155 ( 81%)
[ Info: [ 69%] LOSS: training ≈ 0.2673 validation ≈ 0.2155 ( 81%)
[ Info: [ 70%] LOSS: training ≈ 0.2672 validation ≈ 0.2154 ( 81%)
[ Info: [ 70%] LOSS: training ≈ 0.2672 validation ≈ 0.2153 ( 81%)
[ Info: [ 70%] LOSS: training ≈ 0.2671 validation ≈ 0.2153 ( 81%)
[ Info: [ 71%] LOSS: training ≈ 0.2671 validation ≈ 0.2152 ( 81%)
[ Info: [ 72%] LOSS: training ≈ 0.267  validation ≈ 0.2151 ( 81%)
[ Info: [ 72%] LOSS: training ≈ 0.267  validation ≈ 0.2151 ( 81%)
[ Info: [ 72%] LOSS: training ≈ 0.2669 validation ≈ 0.215  ( 81%)
[ Info: [ 73%] LOSS: training ≈ 0.2669 validation ≈ 0.2149 ( 81%)
[ Info: [ 74%] LOSS: training ≈ 0.2669 validation ≈ 0.2149 ( 81%)
[ Info: [ 74%] LOSS: training ≈ 0.2668 validation ≈ 0.2148 ( 81%)
[ Info: [ 74%] LOSS: training ≈ 0.2668 validation ≈ 0.2147 ( 80%)
[ Info: [ 75%] LOSS: training ≈ 0.2667 validation ≈ 0.2147 ( 80%)
[ Info: [ 76%] LOSS: training ≈ 0.2667 validation ≈ 0.2146 ( 80%)
[ Info: [ 76%] LOSS: training ≈ 0.2667 validation ≈ 0.2146 ( 80%)
[ Info: [ 76%] LOSS: training ≈ 0.2666 validation ≈ 0.2145 ( 80%)
[ Info: [ 77%] LOSS: training ≈ 0.2666 validation ≈ 0.2144 ( 80%)
[ Info: [ 78%] LOSS: training ≈ 0.2666 validation ≈ 0.2144 ( 80%)
[ Info: [ 78%] LOSS: training ≈ 0.2665 validation ≈ 0.2143 ( 80%)
[ Info: [ 78%] LOSS: training ≈ 0.2665 validation ≈ 0.2143 ( 80%)
[ Info: [ 79%] LOSS: training ≈ 0.2665 validation ≈ 0.2142 ( 80%)
[ Info: [ 80%] LOSS: training ≈ 0.2664 validation ≈ 0.2142 ( 80%)
[ Info: [ 80%] LOSS: training ≈ 0.2664 validation ≈ 0.2141 ( 80%)
[ Info: [ 80%] LOSS: training ≈ 0.2664 validation ≈ 0.2141 ( 80%)
[ Info: [ 81%] LOSS: training ≈ 0.2663 validation ≈ 0.214  ( 80%)
[ Info: [ 82%] LOSS: training ≈ 0.2663 validation ≈ 0.214  ( 80%)
[ Info: [ 82%] LOSS: training ≈ 0.2663 validation ≈ 0.2139 ( 80%)
[ Info: [ 82%] LOSS: training ≈ 0.2662 validation ≈ 0.2139 ( 80%)
[ Info: [ 83%] LOSS: training ≈ 0.2662 validation ≈ 0.2138 ( 80%)
[ Info: [ 84%] LOSS: training ≈ 0.2662 validation ≈ 0.2138 ( 80%)
[ Info: [ 84%] LOSS: training ≈ 0.2662 validation ≈ 0.2137 ( 80%)
[ Info: [ 84%] LOSS: training ≈ 0.2661 validation ≈ 0.2137 ( 80%)
[ Info: [ 85%] LOSS: training ≈ 0.2661 validation ≈ 0.2136 ( 80%)
[ Info: [ 86%] LOSS: training ≈ 0.2661 validation ≈ 0.2136 ( 80%)
[ Info: [ 86%] LOSS: training ≈ 0.266  validation ≈ 0.2135 ( 80%)
[ Info: [ 86%] LOSS: training ≈ 0.266  validation ≈ 0.2135 ( 80%)
[ Info: [ 87%] LOSS: training ≈ 0.266  validation ≈ 0.2134 ( 80%)
[ Info: [ 88%] LOSS: training ≈ 0.266  validation ≈ 0.2134 ( 80%)
[ Info: [ 88%] LOSS: training ≈ 0.2659 validation ≈ 0.2133 ( 80%)
[ Info: [ 88%] LOSS: training ≈ 0.2659 validation ≈ 0.2133 ( 80%)
[ Info: [ 89%] LOSS: training ≈ 0.2659 validation ≈ 0.2133 ( 80%)
[ Info: [ 90%] LOSS: training ≈ 0.2659 validation ≈ 0.2132 ( 80%)
[ Info: [ 90%] LOSS: training ≈ 0.2659 validation ≈ 0.2132 ( 80%)
[ Info: [ 90%] LOSS: training ≈ 0.2658 validation ≈ 0.2131 ( 80%)
[ Info: [ 91%] LOSS: training ≈ 0.2658 validation ≈ 0.2131 ( 80%)
[ Info: [ 92%] LOSS: training ≈ 0.2658 validation ≈ 0.213  ( 80%)
[ Info: [ 92%] LOSS: training ≈ 0.2658 validation ≈ 0.213  ( 80%)
[ Info: [ 92%] LOSS: training ≈ 0.2657 validation ≈ 0.213  ( 80%)
[ Info: [ 93%] LOSS: training ≈ 0.2657 validation ≈ 0.2129 ( 80%)
[ Info: [ 94%] LOSS: training ≈ 0.2657 validation ≈ 0.2129 ( 80%)
[ Info: [ 94%] LOSS: training ≈ 0.2657 validation ≈ 0.2129 ( 80%)
[ Info: [ 94%] LOSS: training ≈ 0.2657 validation ≈ 0.2128 ( 80%)
[ Info: [ 95%] LOSS: training ≈ 0.2656 validation ≈ 0.2128 ( 80%)
[ Info: [ 96%] LOSS: training ≈ 0.2656 validation ≈ 0.2127 ( 80%)
[ Info: [ 96%] LOSS: training ≈ 0.2656 validation ≈ 0.2127 ( 80%)
[ Info: [ 96%] LOSS: training ≈ 0.2656 validation ≈ 0.2127 ( 80%)
[ Info: [ 97%] LOSS: training ≈ 0.2656 validation ≈ 0.2126 ( 80%)
[ Info: [ 98%] LOSS: training ≈ 0.2656 validation ≈ 0.2126 ( 80%)
[ Info: [ 98%] LOSS: training ≈ 0.2655 validation ≈ 0.2126 ( 80%)
[ Info: [ 98%] LOSS: training ≈ 0.2655 validation ≈ 0.2125 ( 80%)
[ Info: [ 99%] LOSS: training ≈ 0.2655 validation ≈ 0.2125 ( 80%)
[ Info: [100%] LOSS: training ≈ 0.2655 validation ≈ 0.2125 ( 80%)
[ Info: [100%] LOSS: training ≈ 0.2655 validation ≈ 0.2124 ( 80%)
[ Info: [  0%] LOSS: training ≈ 0.4747
[ Info: [  1%] LOSS: training ≈ 0.4038
[ Info: [  2%] LOSS: training ≈ 0.371 
[ Info: [  2%] LOSS: training ≈ 0.352 
[ Info: [  2%] LOSS: training ≈ 0.3394
[ Info: [  3%] LOSS: training ≈ 0.3303
[ Info: [  4%] LOSS: training ≈ 0.3233
[ Info: [  4%] LOSS: training ≈ 0.3178
[ Info: [  4%] LOSS: training ≈ 0.3132
[ Info: [  5%] LOSS: training ≈ 0.3093
[ Info: [  6%] LOSS: training ≈ 0.3059
[ Info: [  6%] LOSS: training ≈ 0.303 
[ Info: [  6%] LOSS: training ≈ 0.3004
[ Info: [  7%] LOSS: training ≈ 0.298 
[ Info: [  8%] LOSS: training ≈ 0.2959
[ Info: [  8%] LOSS: training ≈ 0.2939
[ Info: [  8%] LOSS: training ≈ 0.2921
[ Info: [  9%] LOSS: training ≈ 0.2905
[ Info: [ 10%] LOSS: training ≈ 0.289 
[ Info: [ 10%] LOSS: training ≈ 0.2875
[ Info: [ 10%] LOSS: training ≈ 0.2862
[ Info: [ 11%] LOSS: training ≈ 0.2849
[ Info: [ 12%] LOSS: training ≈ 0.2838
[ Info: [ 12%] LOSS: training ≈ 0.2826
[ Info: [ 12%] LOSS: training ≈ 0.2816
[ Info: [ 13%] LOSS: training ≈ 0.2806
[ Info: [ 14%] LOSS: training ≈ 0.2796
[ Info: [ 14%] LOSS: training ≈ 0.2787
[ Info: [ 14%] LOSS: training ≈ 0.2779
[ Info: [ 15%] LOSS: training ≈ 0.277 
[ Info: [ 16%] LOSS: training ≈ 0.2763
[ Info: [ 16%] LOSS: training ≈ 0.2755
[ Info: [ 16%] LOSS: training ≈ 0.2748
[ Info: [ 17%] LOSS: training ≈ 0.2741
[ Info: [ 18%] LOSS: training ≈ 0.2734
[ Info: [ 18%] LOSS: training ≈ 0.2728
[ Info: [ 18%] LOSS: training ≈ 0.2722
[ Info: [ 19%] LOSS: training ≈ 0.2716
[ Info: [ 20%] LOSS: training ≈ 0.271 
[ Info: [ 20%] LOSS: training ≈ 0.2705
[ Info: [ 20%] LOSS: training ≈ 0.27  
[ Info: [ 21%] LOSS: training ≈ 0.2695
[ Info: [ 22%] LOSS: training ≈ 0.269 
[ Info: [ 22%] LOSS: training ≈ 0.2685
[ Info: [ 22%] LOSS: training ≈ 0.2681
[ Info: [ 23%] LOSS: training ≈ 0.2677
[ Info: [ 24%] LOSS: training ≈ 0.2673
[ Info: [ 24%] LOSS: training ≈ 0.2669
[ Info: [ 24%] LOSS: training ≈ 0.2665
[ Info: [ 25%] LOSS: training ≈ 0.2661
[ Info: [ 26%] LOSS: training ≈ 0.2657
[ Info: [ 26%] LOSS: training ≈ 0.2654
[ Info: [ 26%] LOSS: training ≈ 0.265 
[ Info: [ 27%] LOSS: training ≈ 0.2647
[ Info: [ 28%] LOSS: training ≈ 0.2644
[ Info: [ 28%] LOSS: training ≈ 0.2641
[ Info: [ 28%] LOSS: training ≈ 0.2638
[ Info: [ 29%] LOSS: training ≈ 0.2635
[ Info: [ 30%] LOSS: training ≈ 0.2632
[ Info: [ 30%] LOSS: training ≈ 0.263 
[ Info: [ 30%] LOSS: training ≈ 0.2627
[ Info: [ 31%] LOSS: training ≈ 0.2625
[ Info: [ 32%] LOSS: training ≈ 0.2622
[ Info: [ 32%] LOSS: training ≈ 0.262 
[ Info: [ 32%] LOSS: training ≈ 0.2618
[ Info: [ 33%] LOSS: training ≈ 0.2615
[ Info: [ 34%] LOSS: training ≈ 0.2613
[ Info: [ 34%] LOSS: training ≈ 0.2611
[ Info: [ 34%] LOSS: training ≈ 0.2609
[ Info: [ 35%] LOSS: training ≈ 0.2607
[ Info: [ 36%] LOSS: training ≈ 0.2605
[ Info: [ 36%] LOSS: training ≈ 0.2603
[ Info: [ 36%] LOSS: training ≈ 0.2602
[ Info: [ 37%] LOSS: training ≈ 0.26  
[ Info: [ 38%] LOSS: training ≈ 0.2598
[ Info: [ 38%] LOSS: training ≈ 0.2597
[ Info: [ 38%] LOSS: training ≈ 0.2595
[ Info: [ 39%] LOSS: training ≈ 0.2593
[ Info: [ 40%] LOSS: training ≈ 0.2592
[ Info: [ 40%] LOSS: training ≈ 0.259 
[ Info: [ 40%] LOSS: training ≈ 0.2589
[ Info: [ 41%] LOSS: training ≈ 0.2588
[ Info: [ 42%] LOSS: training ≈ 0.2586
[ Info: [ 42%] LOSS: training ≈ 0.2585
[ Info: [ 42%] LOSS: training ≈ 0.2584
[ Info: [ 43%] LOSS: training ≈ 0.2582
[ Info: [ 44%] LOSS: training ≈ 0.2581
[ Info: [ 44%] LOSS: training ≈ 0.258 
[ Info: [ 44%] LOSS: training ≈ 0.2579
[ Info: [ 45%] LOSS: training ≈ 0.2578
[ Info: [ 46%] LOSS: training ≈ 0.2577
[ Info: [ 46%] LOSS: training ≈ 0.2576
[ Info: [ 46%] LOSS: training ≈ 0.2574
[ Info: [ 47%] LOSS: training ≈ 0.2573
[ Info: [ 48%] LOSS: training ≈ 0.2572
[ Info: [ 48%] LOSS: training ≈ 0.2572
[ Info: [ 48%] LOSS: training ≈ 0.2571
[ Info: [ 49%] LOSS: training ≈ 0.257 
[ Info: [ 50%] LOSS: training ≈ 0.2569
[ Info: [ 50%] LOSS: training ≈ 0.2568
[ Info: [ 50%] LOSS: training ≈ 0.2567
[ Info: [ 51%] LOSS: training ≈ 0.2566
[ Info: [ 52%] LOSS: training ≈ 0.2565
[ Info: [ 52%] LOSS: training ≈ 0.2565
[ Info: [ 52%] LOSS: training ≈ 0.2564
[ Info: [ 53%] LOSS: training ≈ 0.2563
[ Info: [ 54%] LOSS: training ≈ 0.2562
[ Info: [ 54%] LOSS: training ≈ 0.2562
[ Info: [ 55%] LOSS: training ≈ 0.2561
[ Info: [ 55%] LOSS: training ≈ 0.256 
[ Info: [ 56%] LOSS: training ≈ 0.256 
[ Info: [ 56%] LOSS: training ≈ 0.2559
[ Info: [ 56%] LOSS: training ≈ 0.2558
[ Info: [ 57%] LOSS: training ≈ 0.2558
[ Info: [ 57%] LOSS: training ≈ 0.2557
[ Info: [ 58%] LOSS: training ≈ 0.2556
[ Info: [ 58%] LOSS: training ≈ 0.2556
[ Info: [ 59%] LOSS: training ≈ 0.2555
[ Info: [ 60%] LOSS: training ≈ 0.2555
[ Info: [ 60%] LOSS: training ≈ 0.2554
[ Info: [ 60%] LOSS: training ≈ 0.2554
[ Info: [ 61%] LOSS: training ≈ 0.2553
[ Info: [ 62%] LOSS: training ≈ 0.2553
[ Info: [ 62%] LOSS: training ≈ 0.2552
[ Info: [ 62%] LOSS: training ≈ 0.2552
[ Info: [ 63%] LOSS: training ≈ 0.2551
[ Info: [ 64%] LOSS: training ≈ 0.2551
[ Info: [ 64%] LOSS: training ≈ 0.255 
[ Info: [ 64%] LOSS: training ≈ 0.255 
[ Info: [ 65%] LOSS: training ≈ 0.2549
[ Info: [ 66%] LOSS: training ≈ 0.2549
[ Info: [ 66%] LOSS: training ≈ 0.2548
[ Info: [ 66%] LOSS: training ≈ 0.2548
[ Info: [ 67%] LOSS: training ≈ 0.2548
[ Info: [ 68%] LOSS: training ≈ 0.2547
[ Info: [ 68%] LOSS: training ≈ 0.2547
[ Info: [ 68%] LOSS: training ≈ 0.2546
[ Info: [ 69%] LOSS: training ≈ 0.2546
[ Info: [ 70%] LOSS: training ≈ 0.2546
[ Info: [ 70%] LOSS: training ≈ 0.2545
[ Info: [ 70%] LOSS: training ≈ 0.2545
[ Info: [ 71%] LOSS: training ≈ 0.2545
[ Info: [ 72%] LOSS: training ≈ 0.2544
[ Info: [ 72%] LOSS: training ≈ 0.2544
[ Info: [ 72%] LOSS: training ≈ 0.2544
[ Info: [ 73%] LOSS: training ≈ 0.2543
[ Info: [ 74%] LOSS: training ≈ 0.2543
[ Info: [ 74%] LOSS: training ≈ 0.2543
[ Info: [ 74%] LOSS: training ≈ 0.2542
[ Info: [ 75%] LOSS: training ≈ 0.2542
[ Info: [ 76%] LOSS: training ≈ 0.2542
[ Info: [ 76%] LOSS: training ≈ 0.2541
[ Info: [ 76%] LOSS: training ≈ 0.2541
[ Info: [ 77%] LOSS: training ≈ 0.2541
[ Info: [ 78%] LOSS: training ≈ 0.2541
[ Info: [ 78%] LOSS: training ≈ 0.254 
[ Info: [ 78%] LOSS: training ≈ 0.254 
[ Info: [ 79%] LOSS: training ≈ 0.254 
[ Info: [ 80%] LOSS: training ≈ 0.254 
[ Info: [ 80%] LOSS: training ≈ 0.2539
[ Info: [ 80%] LOSS: training ≈ 0.2539
[ Info: [ 81%] LOSS: training ≈ 0.2539
[ Info: [ 82%] LOSS: training ≈ 0.2539
[ Info: [ 82%] LOSS: training ≈ 0.2538
[ Info: [ 82%] LOSS: training ≈ 0.2538
[ Info: [ 83%] LOSS: training ≈ 0.2538
[ Info: [ 84%] LOSS: training ≈ 0.2538
[ Info: [ 84%] LOSS: training ≈ 0.2537
[ Info: [ 84%] LOSS: training ≈ 0.2537
[ Info: [ 85%] LOSS: training ≈ 0.2537
[ Info: [ 86%] LOSS: training ≈ 0.2537
[ Info: [ 86%] LOSS: training ≈ 0.2537
[ Info: [ 86%] LOSS: training ≈ 0.2536
[ Info: [ 87%] LOSS: training ≈ 0.2536
[ Info: [ 88%] LOSS: training ≈ 0.2536
[ Info: [ 88%] LOSS: training ≈ 0.2536
[ Info: [ 88%] LOSS: training ≈ 0.2536
[ Info: [ 89%] LOSS: training ≈ 0.2535
[ Info: [ 90%] LOSS: training ≈ 0.2535
[ Info: [ 90%] LOSS: training ≈ 0.2535
[ Info: [ 90%] LOSS: training ≈ 0.2535
[ Info: [ 91%] LOSS: training ≈ 0.2535
[ Info: [ 92%] LOSS: training ≈ 0.2535
[ Info: [ 92%] LOSS: training ≈ 0.2534
[ Info: [ 92%] LOSS: training ≈ 0.2534
[ Info: [ 93%] LOSS: training ≈ 0.2534
[ Info: [ 94%] LOSS: training ≈ 0.2534
[ Info: [ 94%] LOSS: training ≈ 0.2534
[ Info: [ 94%] LOSS: training ≈ 0.2534
[ Info: [ 95%] LOSS: training ≈ 0.2533
[ Info: [ 96%] LOSS: training ≈ 0.2533
[ Info: [ 96%] LOSS: training ≈ 0.2533
[ Info: [ 96%] LOSS: training ≈ 0.2533
[ Info: [ 97%] LOSS: training ≈ 0.2533
[ Info: [ 98%] LOSS: training ≈ 0.2533
[ Info: [ 98%] LOSS: training ≈ 0.2533
[ Info: [ 98%] LOSS: training ≈ 0.2532
[ Info: [ 99%] LOSS: training ≈ 0.2532
[ Info: [100%] LOSS: training ≈ 0.2532
[ Info: [100%] LOSS: training ≈ 0.2532
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
[ Info: Baseline mcc: -2.8367742685852565e-17
[ Info: Optimal 1 variables model - mcc ≈ 0.7534
[ Info: Optimal 2 variables model - mcc ≈ 0.7644
[ Info: Optimal 3 variables model - mcc ≈ 0.778
[ Info: Optimal 4 variables model - mcc ≈ 0.8042
[ Info: Optimal 5 variables model - mcc ≈ 0.8089
[ Info: Returning model with 5 variables - mcc ≈ 0.8089
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
[ Info: [ 2 vars.] MCC val. ≈ -0.0
[ Info: [ 3 vars.] MCC val. ≈ 0.772
[ Info: [ 4 vars.] MCC val. ≈ 0.773
[ Info: Optimal var. pool: [1, 12, 18, 7]
┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44
┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
[ Info: [ 0 vars.] MCC val. ≈ -0.0
[ Info: [ 1 vars.] MCC val. ≈ 0.719
[ Info: [ 2 vars.] MCC val. ≈ 0.759
[ Info: [ 3 vars.] MCC val. ≈ 0.773
[ Info: [ 4 vars.] MCC val. ≈ 0.786
[ Info: [ 5 vars.] MCC val. ≈ 0.821
[ Info: [ 6 vars.] MCC val. ≈ 0.842
[ Info: Optimal var. pool: [8, 3, 1, 18, 4, 12]
┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44
[ Info: [19 vars.] MCC val. ≈ -0.0
[ Info: [18 vars.] MCC val. ≈ 0.807
[ Info: Optimal var. pool: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19]
┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44
[ Info: [ 9 vars.] MCC val. ≈ -0.0
[ Info: [ 8 vars.] MCC val. ≈ 0.804
[ Info: [ 7 vars.] MCC val. ≈ 0.81
[ Info: Optimal var. pool: [2, 3, 4, 5, 7, 8, 9]
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
[ Info: [ 2 vars.] MCC val. ≈ -0.0
[ Info: [ 3 vars.] MCC val. ≈ 0.765
[ Info: [ 4 vars.] MCC val. ≈ 0.814
[ Info: [ 5 vars.] MCC val. ≈ 0.821
[ Info: Optimal var. pool: [12, 13, 6, 3, 1]
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
[ Info: [ 0 vars.] MCC val. ≈ -0.0
[ Info: [ 1 vars.] MCC val. ≈ 0.727
[ Info: [ 2 vars.] MCC val. ≈ 0.734
[ Info: [ 3 vars.] MCC val. ≈ 0.754
[ Info: [ 4 vars.] MCC val. ≈ 0.781
[ Info: [ 5 vars.] MCC val. ≈ 0.808
[ Info: Optimal var. pool: [5, 8, 7, 4, 14]
Test Summary: | Pass  Total      Time
Package       |  374    374  26m15.7s
     Testing SDeMo tests passed 

Testing completed after 1631.02s

PkgEval succeeded after 1688.63s