marg_pow

Marginal log-likelihood and posterior density of discrete power law via numerical integration

The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution <doi:10.1111/stan.12355>, of which functions are provided.

Clement Lee

crandep

Network Analysis of Dependencies of CRAN Packages

marg_pow function

<dl><dt>df</dt>
<dd>A data frame with at least two columns, x &amp; count</dd>
<dt>lower</dt>
<dd>Real number greater than 1, lower limit for numerical integration</dd>
<dt>upper</dt>
<dd>Real number greater than lower, upper limit for numerical integration</dd>
<dt>m_alpha</dt>
<dd>Real number (default 0.0), mean of the prior normal distribution for alpha</dd>
<dt>s_alpha</dt>
<dd>Positive real number (default 10.0), standard deviation of the prior normal distribution for alpha</dd>
<dt>by</dt>
<dd>Positive real number, the width of subintervals between lower and upper, for numerical integration and posterior density evaluation</dd></dl>

Arguments

Marginal log-likelihood and posterior density of discrete power law via numerical integration — marg_pow

<dl>

<dt>df</dt>
<dd>A data frame with at least two columns, x &amp; count</dd>


<dt>lower</dt>
<dd>Real number greater than 1, lower limit for numerical integration</dd>


<dt>upper</dt>
<dd>Real number greater than lower, upper limit for numerical integration</dd>


<dt>m_alpha</dt>
<dd>Real number (default 0.0), mean of the prior normal distribution for alpha</dd>


<dt>s_alpha</dt>
<dd>Positive real number (default 10.0), standard deviation of the prior normal distribution for alpha</dd>


<dt>by</dt>
<dd>Positive real number, the width of subintervals between lower and upper, for numerical integration and posterior density evaluation</dd>

</dl>

marg_pow: Marginal log-likelihood and posterior density of discrete power law via numerical integration

Description

Usage

Value

Arguments