rmvhyper_base

Random samples from multivariate hypergeometric

internal

Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG).
To learn how to use it, check the vignettes for a quick tutorial.
Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300>
together with those relevant references from the one listed below.
The package is based on the research developed at the Chair of Systems Design, ETH Zurich.
Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <doi:10.48550/arXiv.1607.02441>.
Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>.
Casiraghi, G., (2017) <doi:10.48550/arXiv.1702.02048>.
Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>.
Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>.
Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>.
Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.

Giona Casiraghi

ghypernet

Fit and Simulate Generalised Hypergeometric Ensembles of Graphs

Vahan Nanumyan

Laurence Brandenberger

Giacomo Vaccario

rmvhyper_base function

<dl><dt>nn</dt>
<dd>number of samples</dd>
<dt>n</dt>
<dd>integer vector of population sizes</dd>
<dt>k</dt>
<dd>integer total draws</dd></dl>

Arguments

Random samples from multivariate hypergeometric — rmvhyper_base

<dl>

<dt>nn</dt>
<dd>number of samples</dd>


<dt>n</dt>
<dd>integer vector of population sizes</dd>


<dt>k</dt>
<dd>integer total draws</dd>

</dl>

rmvhyper_base: Random samples from multivariate hypergeometric

Description

Usage

Value

Arguments