Package evaluation of ParetoSmooth on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T14:36:34.501


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# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 8.24s


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

Installing ParetoSmooth...
   Resolving package versions...
    Updating `~/.julia/environments/v1.11/Project.toml`
  [a68b5a21] + ParetoSmooth v0.7.15
    Updating `~/.julia/environments/v1.11/Manifest.toml`
  [621f4979] + AbstractFFTs v1.5.0
  [66dad0bd] + AliasTables v1.1.3
  [94b1ba4f] + AxisKeys v0.2.15
  [34da2185] + Compat v4.16.0
  [a8cc5b0e] + Crayons v4.1.1
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.18.22
  [e2d170a0] + DataValueInterfaces v1.0.0
  [31c24e10] + Distributions v0.25.118
  [ffbed154] + DocStringExtensions v0.9.4
  [1a297f60] + FillArrays v1.13.0
  [34004b35] + HypergeometricFunctions v0.3.28
  [8197267c] + IntervalSets v0.7.10
  [92d709cd] + IrrationalConstants v0.2.4
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [692b3bcd] + JLLWrappers v1.7.0
  [b964fa9f] + LaTeXStrings v1.4.0
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [be115224] + MCMCDiagnosticTools v0.3.14
  [e80e1ace] + MLJModelInterface v1.11.0
  [e1d29d7a] + Missings v1.2.0
  [356022a1] + NamedDims v1.2.2
  [bac558e1] + OrderedCollections v1.8.0
  [90014a1f] + PDMats v0.11.33
  [a68b5a21] + ParetoSmooth v0.7.15
⌅ [aea7be01] + PrecompileTools v1.2.1
  [21216c6a] + Preferences v1.4.3
  [08abe8d2] + PrettyTables v2.4.0
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [189a3867] + Reexport v1.2.2
  [ae029012] + Requires v1.3.1
  [79098fc4] + Rmath v0.8.0
  [30f210dd] + ScientificTypesBase v3.0.0
  [a2af1166] + SortingAlgorithms v1.2.1
  [276daf66] + SpecialFunctions v2.5.0
  [64bff920] + StatisticalTraits v3.4.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.0
  [2913bbd2] + StatsBase v0.34.4
  [4c63d2b9] + StatsFuns v1.4.0
  [892a3eda] + StringManipulation v0.4.1
  [3783bdb8] + TableTraits v1.0.1
  [bd369af6] + Tables v1.12.0
  [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 v1.6.0
  [7b1f6079] + FileWatching v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.11.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [ca575930] + NetworkOptions v1.2.0
  [44cfe95a] + Pkg v1.11.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization v1.11.0
  [2f01184e] + SparseArrays v1.11.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] + LibCURL_jll v8.6.0+0
  [e37daf67] + LibGit2_jll v1.7.2+0
  [29816b5a] + LibSSH2_jll v1.11.0+1
  [c8ffd9c3] + MbedTLS_jll v2.28.6+0
  [14a3606d] + MozillaCACerts_jll v2023.12.12
  [4536629a] + OpenBLAS_jll v0.3.27+1
  [05823500] + OpenLibm_jll v0.8.5+0
  [bea87d4a] + SuiteSparse_jll v7.7.0+0
  [83775a58] + Zlib_jll v1.2.13+1
  [8e850b90] + libblastrampoline_jll v5.11.0+0
  [8e850ede] + nghttp2_jll v1.59.0+0
  [3f19e933] + p7zip_jll v17.4.0+2
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 4.28s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 307.9s


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# Testing
#

     Testing ParetoSmooth
      Status `/tmp/jl_cQqoS0/Project.toml`
  [94b1ba4f] AxisKeys v0.2.15
  [336ed68f] CSV v0.10.15
  [a93c6f00] DataFrames v1.7.0
  [31c24e10] Distributions v0.25.118
⌅ [366bfd00] DynamicPPL v0.34.2
  [2ab3a3ac] LogExpFunctions v0.3.29
  [c7f686f2] MCMCChains v6.0.7
  [be115224] MCMCDiagnosticTools v0.3.14
  [356022a1] NamedDims v1.2.2
  [a68b5a21] ParetoSmooth v0.7.15
  [08abe8d2] PrettyTables v2.4.0
  [df47a6cb] RData v1.0.0
  [ae029012] Requires v1.3.1
  [10745b16] Statistics v1.11.1
  [2913bbd2] StatsBase v0.34.4
  [4c63d2b9] StatsFuns v1.4.0
  [98d24dd4] TestSetExtensions v3.0.0
  [37e2e46d] LinearAlgebra v1.11.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_cQqoS0/Manifest.toml`
  [47edcb42] ADTypes v1.14.0
  [621f4979] AbstractFFTs v1.5.0
  [80f14c24] AbstractMCMC v5.6.0
⌅ [7a57a42e] AbstractPPL v0.10.1
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.42
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  [66dad0bd] AliasTables v1.1.3
  [dce04be8] ArgCheck v2.5.0
  [a9b6321e] Atomix v1.1.1
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  [39de3d68] AxisArrays v0.4.7
  [94b1ba4f] AxisKeys v0.2.15
  [198e06fe] BangBang v0.4.4
  [9718e550] Baselet v0.1.1
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  [336ed68f] CSV v0.10.15
  [324d7699] CategoricalArrays v0.10.8
  [d360d2e6] ChainRulesCore v1.25.1
  [9e997f8a] ChangesOfVariables v0.1.9
  [944b1d66] CodecZlib v0.7.8
  [38540f10] CommonSolve v0.2.4
  [34da2185] Compat v4.16.0
  [a33af91c] CompositionsBase v0.1.2
  [88cd18e8] ConsoleProgressMonitor v0.1.2
  [187b0558] ConstructionBase v1.5.8
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
  [a93c6f00] DataFrames v1.7.0
  [864edb3b] DataStructures v0.18.22
  [e2d170a0] DataValueInterfaces v1.0.0
  [ab62b9b5] DeepDiffs v1.2.0
  [244e2a9f] DefineSingletons v0.1.2
  [b429d917] DensityInterface v0.4.0
  [31c24e10] Distributions v0.25.118
  [ffbed154] DocStringExtensions v0.9.4
⌅ [366bfd00] DynamicPPL v0.34.2
  [e2ba6199] ExprTools v0.1.10
  [7a1cc6ca] FFTW v1.8.1
  [5789e2e9] FileIO v1.17.0
  [48062228] FilePathsBase v0.9.24
  [1a297f60] FillArrays v1.13.0
  [d9f16b24] Functors v0.5.2
  [34004b35] HypergeometricFunctions v0.3.28
  [22cec73e] InitialValues v0.3.1
  [842dd82b] InlineStrings v1.4.3
  [a98d9a8b] Interpolations v0.15.1
  [8197267c] IntervalSets v0.7.10
  [3587e190] InverseFunctions v0.1.17
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.4
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [692b3bcd] JLLWrappers v1.7.0
  [682c06a0] JSON v0.21.4
⌅ [63c18a36] KernelAbstractions v0.9.31
  [5ab0869b] KernelDensity v0.6.9
  [b964fa9f] LaTeXStrings v1.4.0
  [1d6d02ad] LeftChildRightSiblingTrees v0.2.0
  [6fdf6af0] LogDensityProblems v2.1.2
  [996a588d] LogDensityProblemsAD v1.13.0
  [2ab3a3ac] LogExpFunctions v0.3.29
  [e6f89c97] LoggingExtras v1.1.0
  [c7f686f2] MCMCChains v6.0.7
  [be115224] MCMCDiagnosticTools v0.3.14
  [e80e1ace] MLJModelInterface v1.11.0
  [1914dd2f] MacroTools v0.5.15
  [dbb5928d] MappedArrays v0.4.2
  [128add7d] MicroCollections v0.2.0
  [e1d29d7a] Missings v1.2.0
  [78c3b35d] Mocking v0.8.1
  [356022a1] NamedDims v1.2.2
  [c020b1a1] NaturalSort v1.0.0
  [6fe1bfb0] OffsetArrays v1.16.0
  [bac558e1] OrderedCollections v1.8.0
  [90014a1f] PDMats v0.11.33
  [a68b5a21] ParetoSmooth v0.7.15
  [69de0a69] Parsers v2.8.1
  [2dfb63ee] PooledArrays v1.4.3
⌅ [aea7be01] PrecompileTools v1.2.1
  [21216c6a] Preferences v1.4.3
  [08abe8d2] PrettyTables v2.4.0
  [33c8b6b6] ProgressLogging v0.1.4
  [92933f4c] ProgressMeter v1.10.4
  [43287f4e] PtrArrays v1.3.0
  [1fd47b50] QuadGK v2.11.2
  [df47a6cb] RData v1.0.0
  [b3c3ace0] RangeArrays v0.3.2
  [c84ed2f1] Ratios v0.4.5
  [3cdcf5f2] RecipesBase v1.3.4
  [189a3867] Reexport v1.2.2
  [ae029012] Requires v1.3.1
  [79098fc4] Rmath v0.8.0
  [f2b01f46] Roots v2.2.7
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  [6c6a2e73] Scratch v1.2.1
  [91c51154] SentinelArrays v1.4.8
  [efcf1570] Setfield v1.1.2
  [a2af1166] SortingAlgorithms v1.2.1
  [276daf66] SpecialFunctions v2.5.0
  [171d559e] SplittablesBase v0.1.15
  [90137ffa] StaticArrays v1.9.13
  [1e83bf80] StaticArraysCore v1.4.3
  [64bff920] StatisticalTraits v3.4.0
  [10745b16] Statistics v1.11.1
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  [856f044c] MKL_jll v2025.0.1+1
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  [4607b0f0] SuiteSparse
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  [e66e0078] CompilerSupportLibraries_jll v1.1.1+0
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  [c8ffd9c3] MbedTLS_jll v2.28.6+0
  [14a3606d] MozillaCACerts_jll v2023.12.12
  [4536629a] OpenBLAS_jll v0.3.27+1
  [05823500] OpenLibm_jll v0.8.5+0
  [bea87d4a] SuiteSparse_jll v7.7.0+0
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.11.0+0
  [8e850ede] nghttp2_jll v1.59.0+0
  [3f19e933] p7zip_jll v17.4.0+2
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
Precompiling RData...
  79767.2 ms  ✓ DataFrames
  25301.3 ms  ✓ RData
  2 dependencies successfully precompiled in 106 seconds. 59 already precompiled.
┌ Warning: Conversion of RData.RDummy{0xfe} to Julia is not implemented
└ @ RData ~/.julia/packages/RData/L5u8v/src/convert.jl:198
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
Results of PSIS with 1000 posterior samples and 32 cases.
┌──────────┬────────┬─────────┐
│ pareto_k │    ess │ sup_ess │
├──────────┼────────┼─────────┤
│     0.00 │ 932.56 │  593.21 │
│    -0.11 │ 932.97 │  676.39 │
│    -0.09 │ 959.67 │  612.62 │
│    -0.02 │ 890.89 │  603.11 │
│    -0.20 │ 896.10 │  639.45 │
│    -0.21 │ 862.92 │  634.76 │
│    -0.11 │ 891.36 │  498.36 │
│     0.12 │ 859.63 │  463.32 │
│    -0.11 │ 832.90 │  544.89 │
│    -0.15 │ 926.83 │  655.70 │
│    -0.09 │ 878.46 │  627.06 │
│    -0.14 │ 930.72 │  650.19 │
│    -0.17 │ 871.69 │  619.43 │
│    -0.08 │ 657.84 │  435.02 │
│    -0.23 │ 766.38 │  500.13 │
│    -0.15 │ 827.34 │  517.21 │
│    -0.05 │ 666.16 │  181.44 │
│    -0.02 │ 825.40 │  237.18 │
│     0.08 │ 680.01 │  417.51 │
│    -0.09 │ 803.63 │  249.76 │
│    -0.09 │ 919.80 │  561.78 │
│    -0.07 │ 850.50 │  530.28 │
│    -0.13 │ 878.42 │  505.21 │
│    -0.14 │ 876.93 │  521.13 │
│    -0.25 │ 907.90 │  615.73 │
│    -0.33 │ 721.34 │  519.02 │
│    -0.23 │ 756.54 │  534.52 │
│    -0.04 │ 653.17 │  433.72 │
│    -0.15 │ 875.75 │  460.10 │
│    -0.03 │ 892.88 │  558.17 │
│    -0.14 │ 860.08 │  523.29 │
│    -0.07 │ 942.84 │  679.14 │
└──────────┴────────┴─────────┘

Results of PSIS-LOO-CV with 1000 Monte Carlo samples and 32 data points. Total Monte Carlo SE of 0.09.
┌───────────┬────────┬──────────┬───────┬─────────┐
│           │  total │ se_total │  mean │ se_mean │
├───────────┼────────┼──────────┼───────┼─────────┤
│   cv_elpd │ -83.59 │     4.28 │ -2.61 │    0.13 │
│ naive_lpd │ -80.26 │     3.24 │ -2.51 │    0.10 │
│     p_eff │   3.33 │     1.15 │  0.10 │    0.04 │
└───────────┴────────┴──────────┴───────┴─────────┘

1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   data ∈ 32-element UnitRange{Int64}
And data, 32-element view(::Matrix{Float64}, :, 2) with eltype Float64:
  (1)  0.0061072744750736855
  (2)  0.004363146795897587
  (3)  0.006526999788423257
  (4)  0.004661894109030282
  (5)  0.004376383051878164
  (6)  0.004488223013120136
  (7)  0.009954824189219622
  (8)  0.011046260806556187
  (9)  0.00590364716507522
 (10)  0.004330923121876583
 (22)  0.006986745617704506
 (23)  0.009095361280767532
 (24)  0.009381647012742387
 (25)  0.00649241689210968
 (26)  0.005890809312965013
 (27)  0.005243298687759039
 (28)  0.01041825798235234
 (29)  0.012924297871388943
 (30)  0.006931178799047695
 (31)  0.007685817185588912
 (32)  0.004419477759599199
1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   data ∈ 32-element UnitRange{Int64}
And data, 32-element view(::Matrix{Float64}, :, 4) with eltype Float64:
  (1)  0.00608907355311565
  (2)  0.0043407568992737825
  (3)  0.006503179105035344
  (4)  0.004655827382776443
  (5)  0.004358817507780247
  (6)  0.004470211238538171
  (7)  0.009930085369609364
  (8)  0.011005596450162334
  (9)  0.005892001697566254
 (10)  0.004313396638629304
 (22)  0.006969545084541083
 (23)  0.009073161613614423
 (24)  0.009367710584676845
 (25)  0.006481417218323609
 (26)  0.005867450223377996
 (27)  0.0052291190389437995
 (28)  0.010387753172663014
 (29)  0.01290887943971607
 (30)  0.006914041631817248
 (31)  0.007669769911451155
 (32)  0.0043956280698643525
1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   data ∈ 32-element UnitRange{Int64}
And data, 32-element Vector{Float64}:
  (1)  0.0029846533449772576
  (2)  0.005144805548058289
  (3)  0.003656236820546036
  (4)  0.001302191123968136
  (5)  0.00402178923186136
  (6)  0.004021193513696006
  (7)  0.002488201648646207
  (8)  0.003688070534198871
  (9)  0.0019745367852473726
 (10)  0.004055034461695012
 (22)  0.002464915963607193
 (23)  0.002443751572986937
 (24)  0.0014866035339871561
 (25)  0.0016956708908184956
 (26)  0.003973227541837536
 (27)  0.0027080007407166524
 (28)  0.0029323094484434158
 (29)  0.0011936923838676307
 (30)  0.002475536748266638
 (31)  0.0020900900601441984
 (32)  0.00541110870127946
1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   data ∈ 32-element UnitRange{Int64}
And data, 32-element view(::Matrix{Float64}, :, 2) with eltype Float64:
  (1)  0.0061072744750736855
  (2)  0.004363146795897587
  (3)  0.006526999788423257
  (4)  0.004661894109030282
  (5)  0.004376383051878164
  (6)  0.004488223013120136
  (7)  0.009954824189219622
  (8)  0.011046260806556187
  (9)  0.00590364716507522
 (10)  0.004330923121876583
 (22)  0.006986745617704506
 (23)  0.009095361280767532
 (24)  0.009381647012742387
 (25)  0.00649241689210968
 (26)  0.005890809312965013
 (27)  0.005243298687759039
 (28)  0.01041825798235234
 (29)  0.012924297871388943
 (30)  0.006931178799047695
 (31)  0.007685817185588912
 (32)  0.004419477759599199
1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   data ∈ 32-element UnitRange{Int64}
And data, 32-element view(::Matrix{Float64}, :, 4) with eltype Float64:
  (1)  0.006118354610757832
  (2)  0.0043706522014310915
  (3)  0.006537889382383003
  (4)  0.004670224721440956
  (5)  0.004384063334685183
  (6)  0.0044962867332767375
  (7)  0.009974246082381797
  (8)  0.011063878037574916
  (9)  0.005916133345694694
 (10)  0.004338346463553188
 (22)  0.007000194774934561
 (23)  0.009113741011451335
 (24)  0.009400611436290325
 (25)  0.00650497556449859
 (26)  0.005907253493868622
 (27)  0.0052555085292015895
 (28)  0.010458552987904622
 (29)  0.012950785296398447
 (30)  0.006944432444723544
 (31)  0.007700840100711052
 (32)  0.004427023157689439
1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   data ∈ 32-element UnitRange{Int64}
And data, 32-element Vector{Float64}:
  (1)  -0.0018126083557829427
  (2)  -0.0017187039416284753
  (3)  -0.0016670017386495228
  (4)  -0.0017853640260793151
  (5)  -0.0017534003259228575
  (6)  -0.0017950277865808161
  (7)  -0.0019491023962615822
  (8)  -0.0015935887939201717
  (9)  -0.002112760935400593
 (10)  -0.001712564920900483
 (22)  -0.0019231026940069779
 (23)  -0.0020187416854254856
 (24)  -0.0020193982575730796
 (25)  -0.001932491665420527
 (26)  -0.0027876087492490274
 (27)  -0.0023259493568887823
 (28)  -0.0038602692382739097
 (29)  -0.0020473313333701877
 (30)  -0.0019103518358517739
 (31)  -0.0019527204143546305
 (32)  -0.001705849576119879
2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   statistic ∈ 2-element Vector{Symbol}
→   column ∈ 4-element Vector{Symbol}
And data, 2×4 Matrix{Float64}:
               (:total)  (:se_total)  (:mean)    (:se_mean)
  (:cv_elpd)  -83.5896    4.28394     -2.61218    0.133873
  (:p_eff)      3.32919   1.15225      0.104037   0.0360077
2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   statistic ∈ 2-element view(::Vector{Symbol},...)
→   column ∈ 4-element Vector{Symbol}
And data, 2×4 view(::Matrix{Float64}, [1, 3], :) with eltype Float64:
               (:total)  (:se_total)  (:mean)    (:se_mean)
  (:cv_elpd)  -83.5905    4.2839      -2.6122     0.133872
  (:p_eff)      3.33003   1.15224      0.104063   0.0360076
2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   statistic ∈ 2-element Vector{Symbol}
→   column ∈ 4-element Vector{Symbol}
And data, 2×4 Matrix{Float64}:
              (:total)      (:se_total)  (:mean)      (:se_mean)
  (:cv_elpd)   0.000844084   4.48088e-5   2.63776e-5   1.40027e-6
  (:p_eff)    -0.000844084   4.60622e-6  -2.63776e-5   1.43944e-7
2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   statistic ∈ 2-element Vector{Symbol}
→   column ∈ 4-element Vector{Symbol}
And data, 2×4 Matrix{Float64}:
              (:total)     (:se_total)  (:mean)      (:se_mean)
  (:cv_elpd)   0.00111575   1.08481e-5   3.48671e-5   3.39002e-7
  (:p_eff)    -0.00111575  -1.30619e-5  -3.48671e-5  -4.08185e-7
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: We advise against using `naive_lpd`, as it gives inconsistent and strongly biased estimates. Use `psis_loo` instead.
[ Info: We advise against using `naive_lpd`, as it gives inconsistent and strongly biased estimates. Use `psis_loo` instead.
Precompiling CSV...
   1583.8 ms  ✓ WeakRefStrings
  24252.3 ms  ✓ CSV
  2 dependencies successfully precompiled in 26 seconds. 28 already precompiled.
Precompiling MCMCChains...
   5299.4 ms  ✓ Transducers
   1401.5 ms  ✓ Transducers → TransducersAdaptExt
   3877.9 ms  ✓ AbstractMCMC
   8577.2 ms  ✓ MCMCChains
  4 dependencies successfully precompiled in 21 seconds. 156 already precompiled.
Precompiling BangBangDataFramesExt...
   4624.2 ms  ✓ BangBang → BangBangDataFramesExt
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   4446.2 ms  ✓ Transducers → TransducersDataFramesExt
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Precompiling CategoricalArraysRecipesBaseExt...
   1969.8 ms  ✓ CategoricalArrays → CategoricalArraysRecipesBaseExt
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Precompiling TimeZonesRecipesBaseExt...
   2806.6 ms  ✓ TimeZones → TimeZonesRecipesBaseExt
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Precompiling ParetoSmoothMCMCChainsExt...
  10288.0 ms  ✓ ParetoSmooth → ParetoSmoothMCMCChainsExt
  1 dependency successfully precompiled in 12 seconds. 171 already precompiled.
Precompiling DynamicPPL...
   6792.8 ms  ✓ KernelAbstractions
   2985.3 ms  ✓ AbstractPPL
   2007.4 ms  ✓ KernelAbstractions → SparseArraysExt
   1531.7 ms  ✓ KernelAbstractions → LinearAlgebraExt
  18874.3 ms  ✓ DynamicPPL
   5687.5 ms  ✓ DynamicPPL → DynamicPPLChainRulesCoreExt
  6 dependencies successfully precompiled in 40 seconds. 129 already precompiled.
Precompiling CategoricalArraysJSONExt...
   1144.0 ms  ✓ CategoricalArrays → CategoricalArraysJSONExt
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   7658.0 ms  ✓ DynamicPPL → DynamicPPLMCMCChainsExt
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Precompiling ParetoSmoothDynamicPPLExt...
  11072.4 ms  ✓ ParetoSmooth → ParetoSmoothDynamicPPLExt
  1 dependency successfully precompiled in 14 seconds. 200 already precompiled.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
Results of PSIS with 12000 posterior samples and 50 cases.
┌──────────┬─────────┬─────────┐
│ pareto_k │     ess │ sup_ess │
├──────────┼─────────┼─────────┤
│     0.00 │ 6898.35 │ 4338.09 │
│     0.07 │ 8410.22 │ 3630.25 │
│     0.04 │ 8401.02 │ 3194.29 │
│     0.08 │ 7008.75 │ 4219.63 │
│    -0.02 │ 8306.55 │ 4874.14 │
│     0.01 │ 6911.47 │ 4313.77 │
│     0.03 │ 7535.93 │ 4922.84 │
│     0.06 │ 8378.95 │ 2951.19 │
│    -0.01 │ 7003.67 │ 4372.20 │
│     0.04 │ 8396.74 │ 4432.48 │
│     0.15 │ 7862.84 │ 3896.83 │
│     0.05 │ 8372.33 │ 3897.29 │
│     0.08 │ 8408.94 │ 3637.80 │
│     0.00 │ 7733.78 │ 5033.66 │
│     0.07 │ 8389.45 │ 3696.37 │
│     0.17 │ 7699.52 │ 3915.20 │
│    -0.01 │ 7182.12 │ 4543.73 │
│     0.13 │ 8234.62 │ 3159.75 │
│     0.09 │ 8073.59 │ 2522.58 │
│     0.17 │ 7546.22 │ 3936.81 │
│    -0.01 │ 7151.52 │ 4514.44 │
│    -0.03 │ 6879.05 │ 4317.28 │
│    -0.01 │ 7100.73 │ 4462.55 │
│    -0.00 │ 7089.13 │ 4451.01 │
│     0.17 │ 7243.75 │  955.06 │
│     0.05 │ 8395.96 │ 4291.98 │
│    -0.03 │ 6878.10 │ 4320.02 │
│    -0.06 │ 8103.72 │ 4951.23 │
│     0.02 │ 7399.08 │ 4767.80 │
│     0.05 │ 8361.01 │ 2812.59 │
│    -0.03 │ 6889.52 │ 4308.00 │
│     0.01 │ 8391.21 │ 4645.33 │
│     0.11 │ 7503.89 │ 1012.05 │
│    -0.04 │ 7905.40 │ 5007.82 │
│     0.07 │ 8396.36 │ 3680.76 │
│     0.04 │ 8342.99 │ 2696.84 │
│     0.30 │ 6148.27 │  328.73 │
│     0.01 │ 7324.10 │ 4687.29 │
│     0.02 │ 7445.32 │ 4819.01 │
│     0.08 │ 8125.03 │ 2716.88 │
│     0.04 │ 8396.70 │ 4350.45 │
│     0.14 │ 7251.03 │ 4016.08 │
│     0.05 │ 8345.63 │ 3752.07 │
│     0.03 │ 7451.69 │ 4826.17 │
│     0.05 │ 8396.75 │ 4357.86 │
│     0.15 │ 7521.98 │ 3940.97 │
│     0.05 │ 7638.73 │ 4982.39 │
│     0.16 │ 7851.71 │ 3898.03 │
│    -0.02 │ 6920.22 │ 4314.15 │
│     0.03 │ 7776.22 │ 5055.52 │
└──────────┴─────────┴─────────┘

[ Info: We advise against using `naive_lpd`, as it gives inconsistent and strongly biased estimates. Use `psis_loo` instead.
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
[ Info: Some Pareto k values are slightly high (>.5); some pointwise estimates may be slow to converge or have high variance.
Results of PSIS-LOO-CV with 12000 Monte Carlo samples and 50 data points. Total Monte Carlo SE of 0.042.
┌───────────┬────────┬──────────┬───────┬─────────┐
│           │  total │ se_total │  mean │ se_mean │
├───────────┼────────┼──────────┼───────┼─────────┤
│   cv_elpd │ -62.93 │     6.45 │ -1.26 │    0.13 │
│ naive_lpd │ -59.23 │     4.89 │ -1.18 │    0.10 │
│     p_eff │   3.70 │     1.80 │  0.07 │    0.04 │
└───────────┴────────┴──────────┴───────┴─────────┘

[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
Results of PSIS-LOO-CV with 12000 Monte Carlo samples and 50 data points. Total Monte Carlo SE of 0.025.
┌───────────┬────────┬──────────┬───────┬─────────┐
│           │  total │ se_total │  mean │ se_mean │
├───────────┼────────┼──────────┼───────┼─────────┤
│   cv_elpd │ -69.65 │     4.96 │ -1.39 │    0.10 │
│ naive_lpd │ -66.71 │     4.16 │ -1.33 │    0.08 │
│     p_eff │   2.94 │     0.93 │  0.06 │    0.02 │
└───────────┴────────┴──────────┴───────┴─────────┘

[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
┌ Warning: Some Pareto k values are high (>.7), indicating PSIS has failed to approximate the true distribution.
└ @ ParetoSmooth ~/.julia/packages/ParetoSmooth/FvviB/src/InternalHelpers.jl:50
Results of PSIS-LOO-CV with 12000 Monte Carlo samples and 50 data points. Total Monte Carlo SE of 0.062.
┌───────────┬────────┬──────────┬───────┬─────────┐
│           │  total │ se_total │  mean │ se_mean │
├───────────┼────────┼──────────┼───────┼─────────┤
│   cv_elpd │ -63.97 │     6.52 │ -1.28 │    0.13 │
│ naive_lpd │ -59.06 │     4.64 │ -1.18 │    0.09 │
│     p_eff │   4.91 │     2.02 │  0.10 │    0.04 │
└───────────┴────────┴──────────┴───────┴─────────┘

┌───────┬─────────┬────────┬────────┐
│       │ cv_elpd │ cv_avg │ weight │
├───────┼─────────┼────────┼────────┤
│ m5_1t │    0.00 │   0.00 │   0.74 │
│ m5_3t │   -1.04 │  -0.02 │   0.26 │
│ m5_2t │   -6.73 │  -0.13 │   0.00 │
└───────┴─────────┴────────┴────────┘

Test Summary:   | Pass  Total     Time
ParetoSmooth.jl |   48     48  9m25.0s
     Testing ParetoSmooth tests passed 

Testing completed after 575.9s

PkgEval succeeded after 927.69s