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remstimate: Optimization Frameworks for Tie-Oriented and Actor-Oriented Relational Event Models

A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. Maximum Likelihood Optimization (MLE) is supported. Bayesian estimation is done via Hamiltonian Monte Carlo (HMC).

Version: 3.0.0
Depends: R (≥ 4.0.0)
Imports: Rcpp, remify (≥ 4.0.0), remstats (≥ 4.0.0), trust, mvnfast
LinkingTo: Rcpp, RcppArmadillo, remify (≥ 4.0.0)
Suggests: knitr, rmarkdown, tinytest, survival
Published: 2026-05-13
DOI: 10.32614/CRAN.package.remstimate
Author: Giuseppe Arena ORCID iD [aut, cre], Rumana Lakdawala [aut], Fabio Generoso Vieira [aut], Joris Mulder [aut], Marlyne Meijerink-Bosman [ctb], Diana Karimova [ctb], Mahdi Shafiee Kamalabad [ctb], Roger Leenders [ctb]
Maintainer: Giuseppe Arena <g.arena at uva.nl>
BugReports: https://github.com/TilburgNetworkGroup/remstimate/issues
License: MIT + file LICENSE
URL: https://tilburgnetworkgroup.github.io/remstimate/
NeedsCompilation: yes
CRAN checks: remstimate results

Documentation:

Reference manual: remstimate.html , remstimate.pdf
Vignettes: Modeling relational event networks with remstimate (source, R code)

Downloads:

Package source: remstimate_3.0.0.tar.gz
Windows binaries: r-devel: remstimate_3.0.0.zip, r-release: remstimate_3.0.0.zip, r-oldrel: remstimate_3.0.0.zip
macOS binaries: r-release (arm64): remstimate_3.0.0.tgz, r-oldrel (arm64): remstimate_3.0.0.tgz, r-release (x86_64): remstimate_3.0.0.tgz, r-oldrel (x86_64): remstimate_3.0.0.tgz
Old sources: remstimate archive

Reverse dependencies:

Reverse enhances: texreg

Linking:

Please use the canonical form https://CRAN.R-project.org/package=remstimate to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.