perm

<p>A vector of integers. The maximum value of an integer value starting at 0. Defaults to 1. The number of permutation is defined by <code>max</code>'s length</p>

<p>Produces the possible permutations of a set of nodes</p>

internal

Users can declare binary causal models, update beliefs about causal types given data and calculate arbitrary estimands.  Model definition makes use of 'dagitty' functionality. Updating is implemented in 'stan'. The approach used in 'CausalQueries' is a generalization of the 'biqq' models described in "Mixing Methods: A Bayesian Approach" (Humphreys and Jacobs, 2015, <DOI:10.1017/S0003055415000453>). The conceptual extension makes use of work on probabilistic causal models described in Pearl's Causality (Pearl, 2009, <DOI:10.1017/CBO9780511803161>).

Lily Medina

CausalQueries

Make, Update, and Query Binary Causal Models

Clara Bicalho

Jasper Cooper

Macartan Humphreys

Alan Jacobs

Merlin Heidemanns

Julio Solis

Georgiy Syunyaev

perm function

Produces the possible permutations of a set of nodes — perm

Produces the possible permutations of a set of nodes

perm: Produces the possible permutations of a set of nodes

Description

Usage

Arguments

Value

Examples