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remstats - Computes Statistics for Relational Event History Data

Computes a variety of statistics for relational event models (Meijerink et al., 2022, <doi:10.3758/s13428-022-01821-8>). Relational event models enable researchers to investigate exogenous and endogenous factors, and interactions, influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models.

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cpp

8.16 score 8 stars 2 dependents 59 scripts 729 downloads

remify - Processing and Transforming Relational Event History Data

Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>).

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cppopenmp

7.97 score 4 stars 3 dependents 58 scripts 541 downloads

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

Tools for fitting, diagnosing, and analyzing tie-oriented and actor-oriented relational event models, under both frequentist and Bayesian approaches. The package supports tie-oriented modeling (Butts, 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and an actor-oriented modeling framework (Stadtfeld et al., 2017, <doi:10.15195/v4.a14>), with additional model diagnostics and goodness-of-fit tools. Interfaces to estimation backends provide a range of extensions: random-effects (frailty) relational event models capturing sender, receiver, and dyadic heterogeneity (Juozaitiene & Wit 2024, <doi:10.1007/s11336-024-09952-x>; Mulder & Hoff, 2024, <doi:10.1214/24-AOAS1885>), finite mixture and dyadic latent class models for unobserved dyadic heterogeneity (Lakdawala et al., 2026, <doi:10.1016/j.socnet.2026.06.006>), penalized estimation via the lasso, ridge, and elastic net (Tibshirani, R., 1996, <doi:10.1111/j.2517-6161.1996.tb02080.x>; Karimova et al., 2023, <doi:10.1016/j.socnet.2023.02.006>), and approximate Bayesian regularization (Karimova et al., 2025, <doi:10.1016/j.jmp.2025.102925>). Modeling of events with a duration is also supported (Lakdawala et al., 2026, <doi:10.48550/arXiv.2602.21000>) and moving window relational event models (Mulder & Leenders, 2019, <doi:10.1016/j.chaos.2018.11.027>; Meijerink et al., 2023, <doi:10.1371/journal.pone.0272309>).

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openblascppopenmp

6.88 score 5 stars 1 dependents 31 scripts 811 downloads

remverse - Comprehensive Tools for Relational Event History Data

A unified interface for relational event history analysis. The package re-exports key functions from 'remify', 'remstats', and 'remstimate' to support a streamlined workflow from data processing to model estimation and diagnosis.

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6.40 score 3 stars 7 scripts 312 downloads

remulate - Simulate Dynamic Networks from Relational Event Models

Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 <doi:10.48550/arXiv.2403.19329>). The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant (758791).

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cpp

3.74 score 11 stars 6 scripts 251 downloads