dreach

First argument of D-SEP, given as the column number of the
    node in the adjacency matrix.

Second argument of D-SEP, given as the column number of the
 node in the adjacency matrix (<code>y</code> must be different from <code>x</code>).

Adjacency matrix (coding 0,1,2,3 for no edge, circle,
 arrowhead, tail; e.g., <code>amat[a,b] = 2</code> and <code>amat[b,a] = 3</code>
 implies a -> b)

amat

Logical specifying details should be on output

verbose

Let x and y be two distinct vertices in a mixed graph G. This
 function computes D-SEP(x,y,G), which is defined as follows: A node v is in D-SEP(x,y,G) iff v is not equal to x and there is a collider
 path between x and v in G such that every vertex on this path is an
 ancestor of x or y in G. See p.136 of Sprirtes et al (2000) or Definition 4.1 of Maathuis and
 Colombo (2013).

Functions for causal structure
  learning and causal inference using graphical models. The main algorithms
  for causal structure learning are PC (for observational data without hidden
  variables), FCI and RFCI (for observational data with hidden variables),
  and GIES (for a mix of data from observational studies
  (i.e. observational data) and data from experiments
  involving interventions (i.e. interventional data) without hidden
  variables). For causal inference the IDA algorithm and the generalized
  backdoor criterion is implemented.

dreach: Compute D-SEP(x,y,G)

Description

Usage

Arguments

Value

References

See Also