| dream | R Documentation |
The dream package provides users with helpful functions for relational and event analysis. In particular, dream provides users with helper functions for large relational event analysis, such as recently proposed sampling procedures for creating relational risk sets. Alongside the set of functions for relational event analysis, this package includes functions for the structural analysis of one- and two-mode networks, such as network constraint and effective size measures. This package was developed with support from the National Science Foundation’s (NSF) Human Networks and Data Science Program (HNDS) under award number 2241536 (PI: Diego F. Leal). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
The dream package 'API' is structured into six categories, where the prefix identifies what category the specific function corresponds to (see below):
remstats_
netstats_om_
netstats_tm_
estimate_
simulate_
create_
The remstats_ functions compute relational/network statistics for relational event sequences.
For instance, remstats_fourcycles computes the four-cycles network statistic for a two-mode
relational event sequence. The create_ function creates a risk-set for one- and two-mode
relational event sequences based on a set of sampling procedures. The netstats_om_ series of functions compute
static network statics for one-mode networks
(i.e., netstats_om_pib computes Leal (2025) measure for
potential for intercultural brokerage). The netstats_om_ set of functions compute static network
statics for two-mode networks (i.e., netstats_om_effective
computes Burchard and Cornwell (2018) measure for two-mode
ego effective size). The estimate_ functions estimate relational event models for relational event sequences. Currently,
the only function in this set is estimate_rem_logit, which estimates the ordinal timing relational event model and,
under certain conditions, can estimate a Cox-proportional hazard model for exact timing relational event
models (see Bianchi et al. (2024)
and Butts (2008) for more information on
these models). Finally, the simulate_ functions simulate one-mode relational event sequences based upon
results of a relational event model.
Kevin A. Carson kacarson@arizona.edu, Diego F. Leal dflc@arizona.edu
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