MetricGraph

Description

Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) <doi:10.3150/23-BEJ1647>, Bolin, Kovacs, Kumar and Simas (2023) <doi:10.1090/mcom/3929> and Bolin, Simas and Wallin (2023) <doi:10.48550/arXiv.2304.03190> and <doi:10.48550/arXiv.2304.10372>.

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  installed size is 14.4Mb
  sub-directories of 1Mb or more:
    R      1.1Mb
    libs  12.6Mb

Package suggested but not available for checking: ‘INLA’

  installed size is 14.6Mb
  sub-directories of 1Mb or more:
    R      1.1Mb
    libs  12.8Mb

Package suggested but not available for checking: ‘INLA’

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Package suggested but not available for checking: ‘INLA’

Package suggested but not available for checking: ‘INLA’

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