remstimate-package: remstimate: Optimization Frameworks for Tie-Oriented and...
In remstimate: Optimization Frameworks for Tie-Oriented and Actor-Oriented Relational Event Models
remstimate-package R Documentation
remstimate: Optimization Frameworks for Tie-Oriented and Actor-Oriented Relational Event Models
Description
A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1467-9531.2008.00203.x")}) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.15195/v4.a14")}) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.
Author(s)
Maintainer: Giuseppe Arena g.arena@uva.nl (ORCID)
Authors:
Rumana Lakdawala
Fabio Generoso Vieira
Other contributors:
Marlyne Meijerink-Bosman [contributor]
Diana Karimova [contributor]
Mahdi Shafiee Kamalabad [contributor]
Roger Leenders r.t.a.j.leenders@tilburguniversity.edu [contributor]
Joris Mulder j.mulder3@tilburguniversity.edu [contributor]
See Also
Useful links:
remstimate documentation built on April 4, 2025, 2:31 a.m.
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| remstimate-package | R Documentation |
remstimate: Optimization Frameworks for Tie-Oriented and Actor-Oriented Relational Event Models
Description
A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1467-9531.2008.00203.x")}) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.15195/v4.a14")}) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.
Author(s)
Maintainer: Giuseppe Arena g.arena@uva.nl (ORCID)
Authors:
Rumana Lakdawala
Fabio Generoso Vieira
Other contributors:
Marlyne Meijerink-Bosman [contributor]
Diana Karimova [contributor]
Mahdi Shafiee Kamalabad [contributor]
Roger Leenders r.t.a.j.leenders@tilburguniversity.edu [contributor]
Joris Mulder j.mulder3@tilburguniversity.edu [contributor]
See Also
Useful links:
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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