CompatHelper: bump compat for AbstractPPL to 0.15, (keep existing compat)

[github-actions]

This pull request changes the compat entry for the AbstractPPL package from 0.14.1 to 0.14.1, 0.15.
This keeps the compat entries for earlier versions.

Note: I have not tested your package with this new compat entry.
It is your responsibility to make sure that your package tests pass before you merge this pull request.

[github-actions]

DynamicPPL.jl documentation for PR #1398 is available at:
https://TuringLang.github.io/DynamicPPL.jl/previews/PR1398/

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.30%. Comparing base (d2052a1) to head (d99c0d0).

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1398   +/-   ##
=======================================
  Coverage   82.30%   82.30%           
=======================================
  Files          50       50           
  Lines        3543     3543           
=======================================
  Hits         2916     2916           
  Misses        627      627           

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[github-actions]

Benchmarks @ d99c0d0

Performance

Performance Ratio:
Ratio of time to compute gradient and time to compute log-density.
Warning: results are very approximate! See benchmark notes for more context.

===================================================================================================
                                               eval                       gradient                 
                                            ----------  -------------------------------------------
Model                        dim    linked      primal     FwdDiff    RvsDiff    Mooncake    Enzyme
---------------------------------------------------------------------------------------------------
Simple assume observe*         1     false     5.87 ns       10.19    1207.18       29.00      6.31
Simple assume observe*         1      true     24.2 ns        2.48     320.12        7.02      1.53
Smorgasbord                  201     false     6.41 μs       67.89     125.11        6.30      8.82
Smorgasbord                  201      true     8.83 μs       64.86     122.97        5.30      5.94
Loop univariate 1k          1000     false     19.4 μs      984.20     262.79        7.79      5.76
Loop univariate 1k          1000      true     20.8 μs     1306.11     253.79        7.01      5.38
Multivariate 1k             1000     false     22.2 μs      331.91      77.77        9.23      2.62
Multivariate 1k             1000      true     23.4 μs      248.98      68.85        9.45      2.52
Loop univariate 10k        10000     false    189.0 μs    11037.39     276.65        7.67      5.81
Loop univariate 10k        10000      true    206.0 μs    10467.06     264.81        6.77      5.13
Multivariate 10k           10000     false    201.0 μs     4598.42      87.70       10.99      2.07
Multivariate 10k           10000      true    201.0 μs     4744.23      89.09       10.94      2.08
Dynamic                       15     false     1.41 μs         err      41.82       14.82     10.65
Dynamic                       10      true     1.93 μs        1.86      59.45       18.32     18.50
Submodel*                      1     false     5.87 ns       10.20    1295.54       29.06      6.24
Submodel*                      1      true     5.87 ns       10.26    1408.43       29.67      6.38
LDA                           12      true     22.3 μs        0.49       1.95       33.71       err
===================================================================================================

Rows marked * have t(logdensity) below about 100 ns; their ratios can be dominated by timer floor, fixed overhead, and run-to-run variation. For those rows, raw t(grad) is more meaningful than t(grad)/t(logdensity).

Main @ d2052a1

===================================================================================================
                                               eval                       gradient                 
                                            ----------  -------------------------------------------
Model                        dim    linked      primal     FwdDiff    RvsDiff    Mooncake    Enzyme
---------------------------------------------------------------------------------------------------
Simple assume observe*         1     false     6.24 ns       12.96    1031.68       30.48      6.24
Simple assume observe*         1      true     21.9 ns        3.71     326.90        8.76      1.78
Smorgasbord                  201     false      6.2 μs       71.21     126.30        6.35      6.94
Smorgasbord                  201      true     8.85 μs       73.60     119.37        5.13      4.62
Loop univariate 1k          1000     false     18.1 μs     1256.69     290.07        8.80      6.96
Loop univariate 1k          1000      true     19.9 μs     1741.26     267.38        7.95      6.31
Multivariate 1k             1000     false     22.9 μs      354.09      74.14        8.24      1.97
Multivariate 1k             1000      true     22.9 μs      278.95      74.99        9.53      2.03
Loop univariate 10k        10000     false    176.0 μs    14766.43     313.98        9.16      7.13
Loop univariate 10k        10000      true    195.0 μs    13702.24     294.71        8.16      6.35
Multivariate 10k           10000     false    214.0 μs     5113.87      82.12        9.97      1.85
Multivariate 10k           10000      true    218.0 μs     4405.56      80.92        9.79      1.81
Dynamic                       15     false     1.42 μs         err      39.87       12.40     10.54
Dynamic                       10      true     1.96 μs        1.97      54.67       10.09     16.42
Submodel*                      1     false     6.09 ns       12.90    1224.80       30.88      6.40
Submodel*                      1      true     6.09 ns        8.87    1277.18       30.12      6.31
LDA                           12      true     22.5 μs        0.46       1.95       30.38       err
===================================================================================================

Environment

Julia Version 1.11.9
Commit 53a02c0720c (2026-02-06 00:27 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 7763 64-Core Processor
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)