adapting OptimExt accessing converged

Author: bgctw

.github/workflows/CI.yml

@@ -16,7 +16,7 @@ jobs:
           - Basic
           - JET
         version:
-          - '1.6'
+          - '1.10'
           - '1'
         os:
           - ubuntu-latest
@@ -29,7 +29,7 @@ jobs:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
       - uses: cjdoris/julia-downgrade-compat-action@v1
-        if: ${{ matrix.version == '1.6' }}
+        if: ${{ matrix.version == '1.10' }}
         with:
           skip: Pkg,TOML,Statistics          
       - uses: actions/cache@v4

.gitignore

@@ -12,3 +12,4 @@ docs/Manifest.toml
 /jmd/figures/
 
 test/Manifest.toml
+Manifest-v1.11.toml

Project.toml

@@ -31,7 +31,7 @@ Statistics = "1"
 StatsAPI = "1.6"
 LinearAlgebra = "1.6"
 StatsFuns = "0.9.15, 1"
-julia = "1.6"
+julia = "1.10"
 
 [extras]
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"

ext/DistributionFitsOptimExt.jl

@@ -7,7 +7,7 @@ struct OptimOptimizer <: AbstractDistributionFitOptimizer end
 
 function DistributionFits.optimize(f, ::OptimOptimizer, lower, upper)
     result = Optim.optimize(f, lower, upper)
-    (; minimizer = result.minimizer, converged = result.converged, result)
+    (; minimizer = result.minimizer, converged = Optim.converged(result), result)
 end
 
 function __init__()

src/univariate/continuous/lognormal.jl

@@ -169,7 +169,7 @@ end
 
 
 #---- support LogNormal(-x) of negative values ------------
-const ScaledLogNormal{T} = LocationScale{T, Continuous, LogNormal{T}} where T
+const ScaledLogNormal{T} = LocationScale{T1, Continuous, LogNormal{T}} where {T1, T}
 
 σstar(d::ScaledLogNormal) = exp(params(d.ρ)[2])