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TODO.md

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A lot!
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1. GraphViz (and LightGraph?) plots from the DAG (and MC) model.
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1. LightGraph plots from the DAG (and MC) model.
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2. @DAG (vs. the current Dict formulation).
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3. Latent variable support.
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4. AG/MC graphs and m_separation.
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5. MCMC based pcor_test().
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5. ...
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6. m_separation.
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7. ...
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src/methods/adjustment_sets.jl

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Part of the API, Exported
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"""
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function syms_in_paths(paths, f, l)
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thepaths = deepcopy(paths)
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syms = Symbol[]
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for p in paths
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for p in thepaths
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setdiff!(p, [f, l])
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append!(syms, p)
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unique!(syms)
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# `adjustment_sets`
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Compute the covariance adjustment vertex set.
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$(SIGNATURES)
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Part of the API, Exported
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Computes the covariance adjustment vertex set.
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### Required arguments
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```julia
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* `dag::DAG` : DAG
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* `f::Symbol` : Start variable
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* `l::Symbol` : End variable
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```
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### Optional arguments
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```julia
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* `debug::Bool` : Show debug trace
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```
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### Returns
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```julia
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* `adjustmentsets=Vector{Symbol}[]` : Array of adjustment sets
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```
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# Extended help
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### Acknowledgements
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Original author : Rob J Goedman
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### Licence
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Licenced under: MIT.
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Part of the api, exported.
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"""
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function adjustment_sets(dag::DAG, f::Symbol, l::Symbol; debug=false)
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test/test_descendents_03.jl

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fname = joinpath(ProjDir, "test_descendents_03.dot")
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to_graphviz(dag, fname)
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Sys.isapple() && run(`open -a GraphViz.app $(fname)`)
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#Sys.isapple() && run(`open -a GraphViz.app $(fname)`)
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bs = basis_set(dag)
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as = adjustment_sets(dag, :x2, :y)

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