beta.special.pcObj

x.pos

y.pos

pcObj

Covariance that was used in the pc-object fit

mcov

Matrices that can be precomputed, if
 needed (see code for details on how to precompute)

amat,  amatSkel,  t.amat

This function is DEPRECATED! Use <code><a rd-options="" href="/link/ida?package=pcalg&version=2.6-7" data-mini-rdoc="pcalg::ida">ida</a></code> or
 <code><a rd-options="" href="/link/idaFast?package=pcalg&version=2.6-7" data-mini-rdoc="pcalg::idaFast">idaFast</a></code> instead.

multivariate

models

graphs

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, the Generalized
Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC)
and some related functions are implemented. Functions for incorporating
background knowledge are provided.

Markus Kalisch

pcalg

Methods for Graphical Models and Causal Inference

Alain Hauser

Martin Maechler

Diego Colombo

Doris Entner

Patrik Hoyer

Antti Hyttinen

Jonas Peters

Nicoletta Andri

Emilija Perkovic

Preetam Nandy

Philipp Ruetimann

Daniel Stekhoven

Manuel Schuerch

Marco Eigenmann

beta.special.pcObj function

This function is DEPRECATED! Use <code><a rd-options='' href='ida'>ida</a></code> or
 <code><a rd-options='' href='idaFast'>idaFast</a></code> instead.

Compute set of intervention effects in a fast way — beta.special.pcObj

Compute set of intervention effects in a fast way

beta.special.pcObj: Compute set of intervention effects in a fast way

Description

Usage

Arguments

Value

See Also