Rel 0.1.0

Author: goedman

README.md

@@ -16,7 +16,9 @@
 
 StructuralCausalModels.jl is part of the StatisticalRethinkingJulia eco system and contains functionality to analyse directed acyclic graph (DAG) based causal models as described in [StatisticalRethinking](https://xcelab.net/rm/statistical-rethinking/), [Causal Inference in Statistics](http://bcs.wiley.com/he-bcs/Books?action=index&bcsId=10288&itemId=1119186846) and [Cause and Correlation in Biology](https://www.cambridge.org/core/books/cause-and-correlation-in-biology/247799189B31939D24BC0F61FD59E9BB#).
 
-My initial goal for this package is to have a way to apply SCM ideas to the examples in [StatisticalRethinking.jl](https://github.com/StatisticalRethinkingJulia), i.e. a working version of `basis_set()`, `d_separation()` and `adjustment_sets()`.
+My initial goal for this package is to have a way to apply SCM ideas to the examples in [StatisticalRethinking.jl](https://github.com/StatisticalRethinkingJulia), i.e. a working version of `basis_set()`, `d_separation()` `m_separations()` and `adjustment_sets()`.
+
+From the point of view of above functionality, I believe the package is close to R's `ggm` (including most of Sadeghi's additions). I'm hoping version 1.0.0 has a similar API but many more test cases, including more comparisons with R's `dagitty`.
 
 StructuralCausalModels.jl will be registered and, once registered, can be installed using
 `] add StructuralCausalModels`.

src/methods/m_separation.jl

@@ -75,7 +75,7 @@ function m_separation(d::DAG, f::SymbolList, s::SymbolList;
     debug && println("ar = $ar")
   end
 
-  all(ar[f, s] + ar[s, f] .== 0)
+  all(Array(ar[f, s]) + Array(ar[s, f]) .== 0)
 end
 
 export

test/test_m_separation.jl

@@ -20,7 +20,7 @@ f = [:n1]
 s = [:n4]
 c = [:n3]
 
-ms1 = m_separation(dag, f, s; debug=true)
+ms1 = m_separation(dag, f, s; debug=false)
 ms2 = m_separation(dag, f, s; c=c)
 
 @testset "m_separation" begin