To cite the package:

Adin A, Orozco-Acosta E, Ugarte MD (2022). bigDM: Scalable Bayesian Disease Mapping Models for High-Dimensional Data. R package version 0.4.2, https://github.com/spatialstatisticsupna/bigDM.

To cite the scalable Bayesian models implemented in the function CAR_INLA:

Orozco-Acosta E, Adin A, Ugarte MD (2021). “Scalable Bayesian modeling for smoothing disease mapping risks in large spatial data sets using INLA.” Spatial Statistics, 41, 100496. doi:10.1016/j.spasta.2021.100496.

To cite the scalable Bayesian models implemented in the function STCAR_INLA:

Orozco-Acosta E, Adin A, Ugarte MD (2022). “Parallel and distributed Bayesian modelling for analysing high-dimensional spatio-temporal count data.” 2201.08323, https://arxiv.org/abs/2201.08323.

Corresponding BibTeX entries:

  @Manual{,
    title = {bigDM: Scalable Bayesian Disease Mapping Models for
      High-Dimensional Data},
    author = {A Adin and E Orozco-Acosta and M D Ugarte},
    year = {2022},
    note = {R package version 0.4.2},
    url = {https://github.com/spatialstatisticsupna/bigDM},
  }

  @Article{,
    title = {Scalable Bayesian modeling for smoothing disease mapping
      risks in large spatial data sets using INLA},
    author = {E Orozco-Acosta and A Adin and M D Ugarte},
    year = {2021},
    journal = {Spatial Statistics},
    volume = {41},
    pages = {100496},
    doi = {10.1016/j.spasta.2021.100496},
  }

  @Misc{,
    title = {Parallel and distributed Bayesian modelling for analysing
      high-dimensional spatio-temporal count data},
    author = {E Orozco-Acosta and A Adin and M D Ugarte},
    year = {2022},
    eprint = {2201.08323},
    archiveprefix = {arXiv},
    url = {https://arxiv.org/abs/2201.08323},
  }