marks

Examination marks of 88 students in five subjects.

datasets

Provides functions for defining
mixed graphs containing three types of edges, directed,
undirected and bi-directed, with possibly multiple edges.
These graphs are useful because they capture fundamental
independence structures in multivariate distributions
and in the induced distributions after marginalization
and conditioning.
The package is especially concerned with Gaussian graphical
models for
(i) ML estimation for directed acyclic graphs, undirected and
bi-directed graphs and ancestral graph models
(ii) testing several conditional independencies
(iii) checking global identification of DAG Gaussian models
with one latent variable
(iv) testing Markov equivalences and generating Markov
equivalent graphs of specific types.

Giovanni M Marchetti

Graphical Markov Models with Mixed Graphs

Giovanni M. Marchetti

Mathias Drton

Kayvan Sadeghi

marks function

A data frame with 88 observations on the following 5 variables.<dl>
 <dt>mechanics</dt>
<dd>a numeric vector, mark in Mechanics</dd> <dt>vectors</dt>
<dd>a numeric vector, mark in Vectors</dd> <dt>algebra</dt>
<dd>a numeric vector, mark in Algebra</dd> <dt>analysis</dt>
<dd>a numeric vector, mark in Analysis</dd> <dt>statistics</dt>
<dd>a numeric vector, mark in Statistics</dd> 
</dl>

Format

Mathematics marks — marks

A data frame with 88 observations on the following 5 variables.<dl>
 <dt>mechanics</dt>
<dd>a numeric vector, mark in Mechanics</dd>

 <dt>vectors</dt>
<dd>a numeric vector, mark in Vectors</dd>

 <dt>algebra</dt>
<dd>a numeric vector, mark in Algebra</dd>

 <dt>analysis</dt>
<dd>a numeric vector, mark in Analysis</dd>

 <dt>statistics</dt>
<dd>a numeric vector, mark in Statistics</dd>

 
</dl>

marks: Mathematics marks

Description

Usage

Arguments

Format

Details

References

Examples