Han Chen, Matthew P. Conomos, Duy T. Pham

Likelihood-based Boosting for Generalized mixed models

Likelihood-Based Boosting for Generalized Mixed Models
Likelihood-based boosting approaches for generalized mixed models are provided.

Yaofang Hu [aut, cre],
Wanjie Wang [aut],
Yi Yu [aut]
Functions for graph matching via nodes' degree profiles are provided in this package. The models we can handle include Erdos-Renyi random graphs and stochastic block models(SBM). More details are in the reference paper: Yaofang Hu, Wanjie Wang and Yi Yu (2020) <arXiv:2006.03284>.
c(person("Yaofang", "Hu", role=c("aut", "cre"), email = "yaofang.hu@u.nus.edu"),
person("Wanjie", "Wang", role=c("aut"), email = "staww@nus.edu.sg"),
person("Yi", "Yu", role=c("aut"), email = "yi.yu.2@warwick.ac.uk"))

Generalised Management Strategy Evaluation Simulator
person("A. Bradley", "Duthie", email = "brad.duthie@gmail.com",
role = c("aut", "cre"))
Integrates game theory and ecological theory to construct
social-ecological models that simulate the management of populations and
stakeholder actions. These models build off of a previously developed
management strategy evaluation (MSE) framework to simulate all aspects of
management: population dynamics, manager observation of populations, manager
decision making, and stakeholder responses to management decisions. The
newly developed game-theoretic management strategy evaluation (GMSE)
framework uses genetic algorithms to mimic the decision-making process of
managers and stakeholders under conditions of change, uncertainty, and
conflict.

Integrates game theory and ecological theory to construct
social-ecological models that simulate the management of populations and
stakeholder actions. These models build off of a previously developed
management strategy evaluation (MSE) framework to simulate all aspects of
management: population dynamics, manager observation of populations, manager
decision making, and stakeholder responses to management decisions. The
newly developed generalised management strategy evaluation (GMSE)
framework uses genetic algorithms to mimic the decision-making process of
managers and stakeholders under conditions of change, uncertainty, and
conflict. All simulations can be run using the gmse() function.