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StochBlock (version 0.1.5)

Stochastic Blockmodeling of One-Mode and Linked Networks

Description

Stochastic blockmodeling of one-mode and linked networks as presented in Škulj and Žiberna (2022) . The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

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Version

Install

install.packages('StochBlock')

Monthly Downloads

196

Version

0.1.5

License

GPL (>= 2)

Maintainer

Ale<c5><a1> <c5><bd>iberna

Last Published

July 25th, 2025

Functions in StochBlock (0.1.5)

upAndDownSearch

Perform Up-and-Down Search Optimization
llStochBlock

Function that computes criterion function used in stochastic one-mode and linked blockmodeling. If clu is a list, the method for linked/multilevel networks is applied
stochBlockKMint

A function for using k-means to initialized the stochastic one-mode and linked blockmodeling.
ICLStochBlock

Function that computes integrated classification likelihood based on stochastic one-mode and linked block modeling. If clu is a list, the method for linked/multilevel networks is applied. The support for multirelational networks is not tested.
stochBlockORP

A function for optimizing multiple random partitions using stochastic one-mode and linked blockmodeling. Calls stochBlock for optimizing individual partitions.
stochBlock

Function that performs stochastic one-mode and linked blockmodeling by optimizing a single partition. If clu is a list, the method for linked/multilevel networks is applied
StochBlock-package

StochBlock: Stochastic Blockmodeling of One-Mode and Linked Networks
findActiveParam

Finds the active model's parameters
weightsMlLoglik

Computes weights for parts of the multilevel network based on random errors using the SS approach with complete blocks only (compatible with k-means)