condeptree

This is the function that computes the skeletion tree from data.
The input is a matrix x whose rows are the data vectors.
The sample size n is the number of rows.
The number of variables p is the number of columns
The function outputs the skeleton tree g.

Given a data matrix with rows representing data vectors and columns representing variables, produces a directed polytree for the underlying causal structure. Based on the algorithm developed in Chatterjee and Vidyasagar (2022) <arxiv:2209.07028>. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large.

Sourav Chatterjee

PolyTree

Estimate Causal Polytree from Data

condeptree function

<dl><dt>x</dt>
<dd>The input data matrix.</dd></dl>

Arguments

This is the function that computes the skeletion tree from data.
The input is a matrix x whose rows are the data vectors.
The sample size n is the number of rows.
The number of variables p is the number of columns
The function outputs the skeleton tree g. — condeptree

<dl>

<dt>x</dt>
<dd>The input data matrix.</dd>

</dl>

This is the function that computes the skeletion tree from data.
The input is a matrix x whose rows are the data vectors.
The sample size n is the number of rows.
The number of variables p is the number of columns
The function outputs the skeleton tree g.

condeptree: This is the function that computes the skeletion tree from data. The input is a matrix x whose rows are the data vectors. The sample size n is the number of rows. The number of variables p is the number of columns The function outputs the skeleton tree g.

Description

Usage

Arguments