Adjacency matrix of the observed network

Matrix containing the negative square root of the Euclidean distances between latent positions 
(output of <a rd-options="BLSM" href="/link/lpz_dist?package=BLSM&version=0.1.0&to=BLSM" data-mini-rdoc="BLSM::lpz_dist">lpz_dist</a>)

alpha

BLSM Weights matrix of the observed network

Compute the log-likelihood of the whole observed network based on the
latent positions estimates and the model assumptions. See <a rd-options="BLSM" href="/link/BLSM?package=BLSM&version=0.1.0&to=BLSM" data-mini-rdoc="BLSM::BLSM">BLSM</a> for more information.

Provides a Bayesian latent space
model for complex networks, either weighted or unweighted.
Given an observed input graph, the estimates for the latent coordinates
of the nodes are obtained through a Bayesian MCMC algorithm.
The overall likelihood of the graph depends on a fundamental probability
equation, which is defined so that ties are more likely to exist
between nodes whose latent space coordinates are close.
The package is mainly based on the model by Hoff, Raftery and Handcock (2002)
<doi:10.1198/016214502388618906> and contains some extra features
(e.g., removal of the Procrustean step, weights implemented as
coefficients of the latent distances, 3D plots).
The original code related to the above model was retrieved from
<https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>.
Users can inspect the MCMC simulation, create and customize insightful
graphical representations or apply clustering techniques.

Alberto Donizetti

BLSM

Bayesian Latent Space Model

Francesca Ieva

lpY function

Matrix containing the negative square root of the Euclidean distances between latent positions 
(output of <a rd-options='BLSM' href='lpz_dist'>lpz_dist</a>)

Compute the log-likelihood of the whole observed network based on the
latent positions estimates and the model assumptions. See <a rd-options='BLSM' href='BLSM'>BLSM</a> for more information.

lpY: Network log-likelihood

Description

Usage

Arguments

Value