Integrating Data Exchange and Analysis for Networks ('ideanet')
Find the Convex Hull of Admissible Modularity Partitions (CHAMP)
Community Detection Across Multiple Routines (comm_detect)
Measuring Homophily in Ego Networks (ego_homophily)
Ego Network Cleaning and Measure Calculation (ego_netwrite)
Reshaping Egocentric Data (ego_reshape)
Krackhardt and Stern’s E-I Index (ei_index)
Euclidean Distance (euclidean_distance)
Find the SBM-equivalence iterative map on the CHAMP set of somewhere o...
Selecting Individual Networks from ego_netwrite Output (`get_egograp...
Gather a collection of community detection partitions (`get_partitions...
H-Index (h_index)
Interactive GUI for Working with Sociocentric Networks (ideanetViz)
Agresti's Index of Qualitative Variation (iqv)
Merging Network Canvas CSV Files (nc_merge)
Reading and Reshaping Network Canvas Data (nc_read)
Reading Network Data Files and Initial Cleaning (netread)
Network Cleaning and Variable Calculation (netwrite)
Pearson's Phi (pearson_phi)
Pipe operator
Quadratic Assignment Procedure (qap_run).
Individual to Dyadic variable transformation (qap_setup).
Positional (Role) Analysis in Networks (role_analysis)
A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.