rem: Relational Event Models (REM)

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Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.

Author
Laurence Brandenberger
Date of publication
2016-02-19 23:50:57
Maintainer
Laurence Brandenberger <laurence.brandenberger@eawag.ch>
License
GPL (>= 2)
Version
1.1.2

View on CRAN

Man pages

degreeStat
Calculate (in/out)-degree statistics
eventSequence
Create event sequence
fourCycleStat
Calculate four cycle statistics
inertiaStat
Calculate inertia statistics
reciprocityStat
Calculate reciprocity statistics
rem-package
Fit Relational Event Models (REM)
remRate
Rate function for relational event models
similarityStat
Calculate similarity statistics
triadStat
Calculate triad statistics

Files in this package

rem
rem/inst
rem/inst/CITATION
rem/src
rem/src/temp.cpp
rem/src/rem.cpp
rem/src/RcppExports.cpp
rem/NAMESPACE
rem/R
rem/R/RcppExports.R
rem/R/rem.R
rem/R/temp.R
rem/MD5
rem/DESCRIPTION
rem/man
rem/man/remRate.Rd
rem/man/inertiaStat.Rd
rem/man/reciprocityStat.Rd
rem/man/fourCycleStat.Rd
rem/man/degreeStat.Rd
rem/man/triadStat.Rd
rem/man/eventSequence.Rd
rem/man/similarityStat.Rd
rem/man/rem-package.Rd