Merge pull request #178 from tpapp/tp/remove-jet-workaround

remove workaround for JET.jl

Project.toml

@@ -1,7 +1,7 @@
 name = "DynamicHMC"
 uuid = "bbc10e6e-7c05-544b-b16e-64fede858acb"
 authors = ["Tamas K. Papp <[email protected]>"]
-version = "3.4.2"
+version = "3.4.3"
 
 [deps]
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"

docs/src/worked_example.md

@@ -93,7 +93,7 @@ summarize_tree_statistics(results.tree_statistics)
 
 Usually one would run multiple chains and check convergence and mixing using generic MCMC diagnostics not specific to NUTS.
 
-The specifics of running multiple chains is up to the user: various forms of [parallel computing](https://docs.julialang.org/en/v1/manual/parallel-computing/) can be utilized depending on the problem scale and the hardware available. In the example below we use [multi-threading](https://docs.julialang.org/en/v1/manual/multi-threading), using [ThreadTools.jl](https://github.com/baggepinnen/ThreadTools.jl); other excellent packages are available for threading.
+The specifics of running multiple chains is up to the user: various forms of [parallel computing](https://docs.julialang.org/en/v1/manual/parallel-computing/) can be utilized depending on the problem scale and the hardware available. In the example below we use [multi-threading](https://docs.julialang.org/en/v1/manual/multi-threading/), using [ThreadTools.jl](https://github.com/baggepinnen/ThreadTools.jl); other excellent packages are available for threading.
 
 It is easy to obtain posterior results for use with [MCMCDiagnosticsTools.jl](https://github.com/TuringLang/MCMCDiagnosticTools.jl/) with [`stack_posterior_matrices`](@ref):
 

test/jet.jl

@@ -1,5 +1,4 @@
 using JET
-insert!(LOAD_PATH, 2, "..")
 @testset "static analysis with JET.jl" begin
     @test isempty(JET.get_reports(report_package(DynamicHMC, target_modules=(DynamicHMC,))))
 end