generateA

Expected number of neighbors, to be passed to 
<code><a rd-options="pcalg" href="/link/randDAG?package=backShift&version=0.1.4.3&to=pcalg" data-mini-rdoc="pcalg::randDAG">randDAG</a></code>.

expNumNeigh

Minimal edge coefficient. The absolute magnitude of the 
coefficients will be sampled uniformly at random from the 
range \([minCoef, maxCoef]\).

minCoef

Maximal edge coefficient. The absolute magnitude of the 
coefficients will be sampled uniformly at random from the 
range \([minCoef, maxCoef]\).

maxCoef

If <code>TRUE</code>, connectivity matrix will contain at least one
cycle.

cyclic

If <code>TRUE</code>, comments will be printed.

verbose

Generates a connectivity matrix A with cycle product smaller 
 than 1.

Code for 'backShift', an algorithm to estimate the connectivity
matrix of a directed (possibly cyclic) graph with hidden variables. The
underlying system is required to be linear and we assume that observations
under different shift interventions are available. For more details,
see <arXiv:1506.02494>.

Christina Heinze-Deml

backShift

Learning Causal Cyclic Graphs from Unknown Shift Interventions

generateA function

Expected number of neighbors, to be passed to 
<code><a rd-options='pcalg' href='randDAG'>randDAG</a></code>.

generateA: Generates a connectivity matrix A.

Description

Usage

Arguments

Value

Details