error upon `data[:, "y"] ~ MvNormal(...)` in Turing `v0.42.8`

[SimonEnsemble]

Minimal working example

using DataFrames, Turing

	# my data
	my_data = DataFrame(
		"x" => [0.0, 1.0, 2.0],
		"y" => [0.0, 5.0, 10.0]
	)
	
	@model function my_model_def(data::DataFrame)
		m ~ Uniform(0.0, 10.0)

		#=
		option 1 (works)
		=#
		# for i = 1:nrow(data)
		# 	data[i, "y"] ~ Normal(m * data[i, "x"], 0.1)
		# end

		#=
		option 2 (gives error)
		=#
		# data[:, "y"] ~ Normal.(m * data[:, "x"], 0.1)

		#=
		option 3 (gives error)
		=#
		data[:, "y"] ~ MvNormal(m * data[:, "x"], 0.1 * I(3))
		
		return nothing
	end
	
	my_model = my_model_def(my_data) # works just fine
	
	sample(my_model, NUTS(), MCMCThreads(), 12, 2) # breaks! :(

Description

I tried to follow this performance tip by using a multi-variate normal distribution instead of one Normal distribution for each data point in my data frame within a loop. however, I get an error that is not informative, since my_data[:, "y"] works fine.

I apologize if I am writing the MvNormal out incorrectly.

Error message
ArgumentError: invalid index: "y" of type String
Stack trace
Here is what happened, the most recent locations are first:
to_index(i::String)
from 
indices.jl:315
to_index(A::DataFrames.DataFrame, i::String)
from 
indices.jl:292
to_indices
from 
indices.jl:368
to_indices
from 
multidimensional.jl:919
to_indices
from 
indices.jl:359
concretize(I::Accessors.IndexLens{…}, x::DataFrames.DataFrame) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
varname.jl:289
my_model_def(__model__::DynamicPPL.Model{…}, __varinfo__::DynamicPPL.OnlyAccsVarInfo{…}, data::DataFrames.DataFrame) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
[This cell: line 8](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#78b501ca-695b-4cb6-b624-2894474a9ebf)
	
	@model function my_model_def(data::DataFrame)
		m ~ Uniform(0.0, 10.0)
_evaluate!!(model::DynamicPPL.Model{…}, varinfo::DynamicPPL.OnlyAccsVarInfo{…}) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
model.jl:997
evaluate!!(model::DynamicPPL.Model{…}, varinfo::DynamicPPL.OnlyAccsVarInfo{…}) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
model.jl:983
#check_model_and_trace#4(model::DynamicPPL.Model{…}, varinfo::DynamicPPL.OnlyAccsVarInfo{…}; error_on_failure::Bool) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
debug_utils.jl:430
#check_model#5(model::DynamicPPL.Model{…}, varinfo::DynamicPPL.OnlyAccsVarInfo{…}; error_on_failure::Bool) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
debug_utils.jl:453
_check_model(model::DynamicPPL.Model{…}) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
abstractmcmc.jl:3
_check_model(model::DynamicPPL.Model{…}, ::Turing.Inference.NUTS{…}) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
abstractmcmc.jl:10
#sample#4(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{…}, spl::Turing.Inference.NUTS{…}, ensemble::AbstractMCMC.MCMCThreads, N::Int64, n_chains::Int64; chain_type::Type, check_model::Bool, initial_params::Vector{…}, kwargs::@Kwargs{}) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
abstractmcmc.jl:123
sample
from 
abstractmcmc.jl:111
#sample#3
from 
abstractmcmc.jl:106
sample(model::DynamicPPL.Model{…}, alg::Turing.Inference.NUTS{…}, ensemble::AbstractMCMC.MCMCThreads, N::Int64, n_chains::Int64) [...show types...](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#)
from 
abstractmcmc.jl:98
from 
[This cell: line 33](http://localhost:1234/edit?id=cdecece4-0939-11f1-8175-4fda468a67dc#78b501ca-695b-4cb6-b624-2894474a9ebf)
	
	sample(my_model, NUTS(), MCMCThreads(), 12, 2) # breaks! :(
end

Julia version info

Julia Version 1.12.5
Commit 5fe89b8ddc1 (2026-02-09 16:05 UTC)
Build Info:
  Official https://julialang.org release
Platform Info:
  OS: macOS (arm64-apple-darwin24.0.0)
  CPU: 8 × Apple M1
  WORD_SIZE: 64
  LLVM: libLLVM-18.1.7 (ORCJIT, apple-m1)
  GC: Built with stock GC
Threads: 1 default, 1 interactive, 1 GC (on 4 virtual cores)

Manifest

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[penelopeysm]

That's a BangBang issue again.

[penelopeysm]

Same thing as before, I think: this needs either an upstream fix, or a temporary downgrade to DynamicPPL 0.39.4.

[SimonEnsemble]

I thought this was a different problem since data[i, "y"] ~ Normal(m * data[i, "x"], 0.1) is working.

I did add DynamicPPL@0.39.4 and now my st --manifest says:

⌃ [366bfd00] DynamicPPL v0.39.4

yet the error persists.

anyway, I will just avoid that syntax for now until upstream fix, thanks!

[penelopeysm]

Ooh, okay, this is indeed a different bug, and not BangBang's fault. I'll look into it, but it might be possible that the fix will have to wait until the next Turing minor version.

[penelopeysm]

The reason is because the issue is with AbstractPPL 0.13. There's a newer version of AbstractPPL 0.14, which completely overhauls some of the code that triggers this error. However, integrating that into Turing will require integrating it into DynamicPPL (that's done already, but only on v0.40); and then Turing needs to be made to work with DPPL 0.40.

[penelopeysm]

AbstractPPL 0.14 indeed does not have this error, but then it runs into a BangBang error. Using the current breaking branch of DynamicPPL (TuringLang/DynamicPPL.jl@d7d3b31):

using DataFrames, DynamicPPL, Distributions, LinearAlgebra

d = DataFrame(
    "x" => [0.0, 1.0, 2.0],
    "y" => [0.0, 5.0, 10.0]
)
@model function m(data::DataFrame)
    m ~ Uniform(0.0, 10.0)
    data[:, "y"] ~ MvNormal(m * data[:, "x"], I)
    return nothing
end

julia> m(d)()
ERROR: MethodError: no method matching possible(::typeof(BangBang._setindex!), ::DataFrame, ::SubArray{…}, ::Colon, ::String)
The function `possible` exists, but no method is defined for this combination of argument types.

Closest candidates are:
  possible(::typeof(BangBang._setindex!), ::C, ::T, ::Any...) where {T<:AbstractArray, C<:(AbstractArray{T})}
   @ BangBang ~/ppl/BangBang.jl/src/base.jl:541
  possible(::typeof(BangBang._setindex!), ::C, ::T, ::Any...) where {T<:AbstractArray, C<:AbstractArray}
   @ BangBang ~/ppl/BangBang.jl/src/base.jl:512
  possible(::typeof(BangBang._setindex!), ::C, ::T, ::Any...) where {T, C<:(AbstractArray{T})}
   @ BangBang ~/ppl/BangBang.jl/src/base.jl:532
  ...

Stacktrace:
  [1] may
    @ ~/.julia/packages/BangBang/bY1RJ/src/core.jl:7 [inlined]
  [2] setindex!!
    @ ~/.julia/packages/BangBang/bY1RJ/src/base.jl:478 [inlined]
  [3] set(obj::DataFrame, l::AbstractPPL._Optic!!{…}, newval::SubArray{…})
    @ AbstractPPL ~/.julia/packages/AbstractPPL/s2yZd/src/varname/optic.jl:556
[...]

[penelopeysm]

JuliaFolds2/BangBang.jl#48 should fix the BangBang issue, and together with the breaking release of DPPL, the above now works; so all you have to do is wait for the next release of Turing. With some luck, hopefully that will land next week.