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blockmodeling (version 1.0.0)

Generalized and Classical Blockmodeling of Valued Networks

Description

This is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: iberna (2007), iberna (2008), iberna (2014).

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Version

Install

install.packages('blockmodeling')

Monthly Downloads

1,223

Version

1.0.0

License

GPL (>= 2)

Maintainer

Ales Ziberna

Last Published

July 1st, 2020

Functions in blockmodeling (1.0.0)

Function for extraction of some elements for objects, returend by functions for Generalized blockmodeling

Generalized matrix multiplication

Functions for loading and writing Pajek files

An R package for Generalized and classical blockmodeling of valued networks

A formating function for numbers

Citation data between social work journals for the 1985-86 period

Computing the threshold

funByBlocks.default

Computation of function values by blocks

Functions for Generalized blockmodeling for valued networks

REGE - Algorithms for compiting (dis)similarities in terms of regular equivalnece

The notes borrowing network between social-informatics students

Function for iterated row and column normalization of valued matrices

The function for generating random partitions

A wrapper for function gplot - Two-Dimensional Visualization of Graphs

Two-mode network conversions

Functions for listing all possible partitions or just counting the number of them

Reordering an image matrix of the blockmodel (or an error matrix based on new and old partition

Optimizing a set of partitions based on the value of a criterion function The function optimizes a set of partitions based on the value of a criterion function (see critFunC for details on the criterion function) for a given network and blockmodel for Generalized blockmodeling (<U+017D>iberna, 2007) based on other parameters (see below). The optimization is done through local optimization, where the neighborhood of a partition includes all partitions that can be obtained by moving one unit from one cluster to another or by exchanging two units (from different clusters). A list of paritions can or the number of clusters and a number of partitions to generate can be specified (optParC

Sum of Squared deviations from the mean and sum of Absolute Deviations from the median

Comparing partitions

Functions for plotting a partitioned matrix (representing the network)

Computes distances in terms of Structural equivalence (Lorrain & White, 1971)