Chapter 10 Packages used in the book
We have included below the information about the packages used when
compiling this book. This will be useful when trying to reproduce the results
and figures. Different versions of R and the packages shown below may produce
slightly different results. Furthermore, the architecture may also cause
minor differences in the results of model fitting.
## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 11 (bullseye)
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.13.so
##
## attached base packages:
## [1] grid parallel stats graphics grDevices utils
## [7] datasets methods base
##
## other attached packages:
## [1] survival_3.2-11 scales_1.1.1
## [3] splancs_2.01-42 spelling_2.2
## [5] spdep_1.1-8 sf_1.0-1
## [7] spData_0.3.10 spatstat_2.2-0
## [9] spatstat.linnet_2.3-0 spatstat.core_2.3-0
## [11] rpart_4.1-15 nlme_3.1-152
## [13] spatstat.geom_2.2-2 spatstat.data_2.1-0
## [15] rgeos_0.5-5 rgdal_1.5-23
## [17] RandomFields_3.3.8 RandomFieldsUtils_0.5.3
## [19] osmar_1.1-7 geosphere_1.5-10
## [21] RCurl_1.98-1.3 XML_3.99-0.6
## [23] maptools_1.1-1 mapdata_2.3.0
## [25] maps_3.3.0 latticeExtra_0.6-29
## [27] lattice_0.20-41 inlabru_2.3.1
## [29] ggplot2_3.3.5 gridExtra_2.3
## [31] evd_2.3-3 deldir_0.2-10
## [33] RColorBrewer_1.1-2 fields_12.5
## [35] viridis_0.6.1 viridisLite_0.4.0
## [37] spam_2.7-0 dotCall64_1.0-1
## [39] knitr_1.33 INLA_21.02.23
## [41] sp_1.4-5 foreach_1.5.1
## [43] Matrix_1.3-2
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 spatstat.sparse_2.0-0
## [3] gmodels_2.18.1 tools_4.0.5
## [5] bslib_0.2.5.1 utf8_1.2.1
## [7] R6_2.5.0 KernSmooth_2.23-18
## [9] DBI_1.1.1 mgcv_1.8-34
## [11] colorspace_2.0-2 raster_3.4-13
## [13] withr_2.4.2 tidyselect_1.1.1
## [15] compiler_4.0.5 expm_0.999-6
## [17] bookdown_0.22 sass_0.4.0
## [19] classInt_0.4-3 proxy_0.4-26
## [21] goftest_1.2-2 stringr_1.4.0
## [23] digest_0.6.27 foreign_0.8-81
## [25] spatstat.utils_2.2-0 rmarkdown_2.9
## [27] jpeg_0.1-8.1 pkgconfig_2.0.3
## [29] htmltools_0.5.1.1 rlang_0.4.11
## [31] jquerylib_0.1.4 generics_0.1.0
## [33] jsonlite_1.7.2 gtools_3.9.2
## [35] dplyr_1.0.7 magrittr_2.0.1
## [37] Rcpp_1.0.7 munsell_0.5.0
## [39] fansi_0.5.0 abind_1.4-5
## [41] lifecycle_1.0.0 stringi_1.7.3
## [43] yaml_2.2.1 MASS_7.3-54
## [45] gdata_2.18.0 crayon_1.4.1
## [47] splines_4.0.5 tensor_1.5
## [49] pillar_1.6.1 boot_1.3-27
## [51] codetools_0.2-18 LearnBayes_2.15.1
## [53] glue_1.4.2 evaluate_0.14
## [55] png_0.1-7 vctrs_0.3.8
## [57] gtable_0.3.0 purrr_0.3.4
## [59] polyclip_1.10-0 xfun_0.24
## [61] e1071_1.7-7 coda_0.19-4
## [63] class_7.3-18 tibble_3.1.2
## [65] iterators_1.0.13 units_0.7-2
## [67] ellipsis_0.3.2End.
Abrahamsen, P. 1997. “A Review of Gaussian Random Fields and Correlation Functions.” Norwegian Computing Center report No. 917.
Andersen, P. K., and R. D. Gill. 1982. “Cox’s Regression Model for Counting Processes: A Large Sample Study.” The Annals of Statistics 10 (4): 1100–1120.
Assunção, J. J., D. Gamerman, and R. M. Assunção. 1999. “Regional Differences in Factor Productivities of Brazilian Agriculture: A Space-Varying Parameter Approach.” Universidade Federal do Rio de Janeiro, Statistical Laboratory.
Assunção, R. M., J. E. Potter, and S. M. Cavenaghi. 2002. “A Bayesian Space Varying Parameter Model Applied to Estimating Fertility Schedules.” Statistics in Medicine 21: 2057–75.
Baddeley, A., E. Rubak, and R. Turner. 2015. Spatial Point Patterns: Methodology and Applications with R. Boca Raton, FL: Chapman & Hall/CRC.
Bailey, T. C., and A. C. Gatrell. 1995. Interactive Spatial Data Analysis. Harlow, UK: Longman Scientific & Technical.
Bakka, Haakon. 2018. “How to Solve the Stochastic Partial Differential Equation That Gives a Matérn Random Field Using the Finite Element Method.” arXiv Preprint arXiv:1803.03765.
Bakka, H., H. Rue, G.-A. Fuglstad, A. Riebler, D. Bolin, E. Krainski, D. Simpson, and F. Lindgren. 2018. “Spatial Modelling with R-INLA: A Review.” WIREs Comput Stat 10 (6): 1–24. https://doi.org/10.1002/wics.1443.
Bakka, H., J. Vanhatalo, J. Illian, D. Simpson, and H. Rue. 2016. “Non-stationary Gaussian models with physical barriers.” ArXiv E-Prints, August. http://arxiv.org/abs/1608.03787.
Banerjee, S., B. P. Carlin, and A. E. Gelfand. 2014. Hierarchical Modeling and Analysis for Spatial Data. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC.
Becker, R. A., A. R. Wilks, and R. Brownrigg. 2016. Mapdata: Extra Map Databases. https://CRAN.R-project.org/package=mapdata.
Besag, J. 1981. “On a System of Two-Dimensional Recurrence Equations.” J. R. Statist. Soc. B 43 (3): 302–9.
Bivand, R., T. Keitt, and B. Rowlingson. 2017. Rgdal: Bindings for the ’Geospatial’ Data Abstraction Library. https://CRAN.R-project.org/package=rgdal.
Bivand, R., J. Nowosad, and R. Lovelace. 2018. SpData: Datasets for Spatial Analysis. https://CRAN.R-project.org/package=spData.
Bivand, R., and G. Piras. 2015. “Comparing Implementations of Estimation Methods for Spatial Econometrics.” Journal of Statistical Software 63 (18): 1–36. https://www.jstatsoft.org/v63/i18/.
Bivand, R., and C. Rundel. 2017. Rgeos: Interface to Geometry Engine - Open Source (’Geos’). https://CRAN.R-project.org/package=rgeos.
Bivand, R. S., E. J. Pebesma, and V. Gomez-Rubio. 2013. Applied Spatial Data Analysis with R. 2nd ed. Springer, NY. http://www.asdar-book.org/.
Blangiardo, M., and M. Cameletti. 2015. Spatial and SpatioTemporal Bayesian Models with R-INLA. Chichester, UK: John Wiley & Sons, Ltd.
Bowman, A. W., I. Gibson, E. M. Scott, and E. Crawford. 2010. “Interactive Teaching Tools for Spatial Sampling.” Journal of Statistical Software 36 (13): 1–17. http://www.jstatsoft.org/v36/i13/.
Box, G. E., and N. R. Draper. 2007. Response Surfaces, Mixtures, and Ridge Analyses. 2nd ed. Hoboken, NJ: Wiley-Interscience.
Brenner, S. C., and R. Scott. 2007. The Mathematical Theory of Finite Element Methods. 3rd ed. Springer, New York.
Cameletti, M., F. Lindgren, D. Simpson, and H. Rue. 2013. “Spatio-Temporal Modeling of Particulate Matter Concentration Through the Spde Approach.” Advances in Statistical Analysis 97 (2): 109–31.
Carlin, B. P., and T. A. Louis. 2008. Bayesian Methods for Data Analysis. 3rd ed. Boca Raton, FL: Chapman & Hall/CRC.
Casson, E., and S. Coles. 1999. “Spatial Regression Models for Extremes.” Extremes 1 (4): 449–68.
Castro-Camilo, D., and M. de Carvalho. 2017. “Spectral Density Regression for Bivariate Extremes.” Stochastic Environmental Research and Risk Assessment 31 (7): 1603–13.
Castro-Camilo, D., M. de Carvalho, and J. Wadsworth. 2018. “Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets.” Annals of Applied Statistics 12 (1): 283–309.
Chang, W., J. Cheng, J. J. Allaire, Y. Xie, and J. McPherson. 2018. Shiny: Web Application Framework for R. https://CRAN.R-project.org/package=shiny.
Ciarlet, P. G. 1978. The Finite Element Method for Elliptic Problems. Amsterdam: North-Holland.
Coles, S. 2001. An Introduction to Statistical Modeling of Extreme Values. Vol. 208. London: Springer-Verlag.
Cooley, D., D. Nychka, and P. Naveau. 2007. “Bayesian Spatial Modeling of Extreme Precipitation Return Levels.” Journal of the American Statistical Association 102 (479): 824–40.
Cressie, N. 1993. Statistics for Spatial Data. New York: Wiley.
Cressie, N., and C. K. Wikle. 2011. Statistics for Spatio-Temporal Data. Hoboken, NJ: John Wiley & Sons, Inc.
Davison, A. C., and R. L. Smith. 1990. “Models for Exceedances over High Thresholds (with Discussion).” Journal of the Royal Statistical Society: Series B, 393–442.
de Berg, M., O. Cheong, M. van Kreveld, and M. Overmars. 2008. Computational Geometry. 3rd ed. Berlin Heidelberg: Springer-Verlag.
Diggle, P. J. 2013. Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. 3rd ed. Boca Raton, FL: Chapman & Hall/CRC Press.
———. 2014. Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. 3rd ed. Boca Raton, FL: CRC Press, Taylor & Francis Group.
Diggle, P. J., R. Menezes, and T-L. Su. 2010. “Geostatistical Inference Under Preferential Sampling.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 59 (2): 191–232. https://doi.org/10.1111/j.1467-9876.2009.00701.x.
Diggle, P. J., and P. J. Ribeiro Jr. 2007. Model-Based Geostatistics. Springer Series in Statistics. Hardcover.
Eugster, Manuel J. A., and Thomas Schlesinger. 2013. “Osmar: OpenStreetMap and R.” The R Journal 5 (1): 53–63. http://journal.r-project.org/archive/2013/RJ-2013-005/index.html.
Fuglstad, G-A., D. Simpson, F. Lindgren, and H. Rue. 2018. “Constructing Priors That Penalize the Complexity of Gaussian Random Fields.” Journal of the American Statistical Association to appear. https://doi.org/10.1080/01621459.2017.1415907.
Gamerman, D., A. R. B. Moreira, and H. Rue. 2003. “Space-Varying Regression Models: Specifications and Simulation.” Computational Statistics & Data Analysis - Special Issue: Computational Econometrics 42 (3): 513–33.
Gelfand, A. E., H-J Kim, C. F. Sirmans, and S. Banerjee. 2003. “Spatial Modeling with Spatially Varying Coefficient Processes.” Journal of the American Statistical Association 98 (462): 387–96.
Gelfand, A. E., A. M. Schmidt, and C. F. Sirmans. 2002. “Multivariate Spatial Process Models: Conditional and Unconditional Bayesian Approaches Using Coregionalization.” Center for Real Estate and Urban Economic Studies, University of Connecticut.
Gilks, W. R., S. Richardson, and D. J. Spiegelhalter. 1996. Markov Chain Monte Carlo in Practice. Boca Raton, FL: Chapman & Hall.
Gómez-Rubio, V. 2019. Bayesian Inference with INLA. Chapman & Hall/CRC.
Guibas, Leonidas J., D. E. Knuth, and M. Sharir. 1992. “Randomized Incremental Construction of Delaunay and Voronoi Diagrams.” Algorithmica 7 (1–6): 381–413.
Haining, R. 2003. Spatial Data Analysis: Theory and Practice. Cambridge University Press.
Henderson, R., S. Shimakura, and D. Gorst. 2003. “Modeling Spatial Variation in Leukemia Survival Data.” JASA 97 (460): 965–72.
Holford, T. R. 1980. “The Analysis of Rates and of Survivorship Using Log-Linear Models.” Biometrics 36: 299–305.
Ibrahim, J. G., M-H. Chen, and D. Sinha. 2001. Bayesian Survival Analysis. New York: Springer-Verlag.
Illian, J. B., S. H. Sørbye, and H. Rue. 2012. “A Toolbox for Fitting Complex Spatial Point Process Models Using Integrated Nested Laplace Approximation (INLA).” Annals of Applied Statistics 6 (4): 1499–1530.
Illian, J., A. Penttinen, H. Stoyan, and D. Stoyan. 2008. Statistical Analysis and Modelling of Spatial Point Patterns. Chichester, UK: Wiley & Sons.
Ingebrigtsen, R., F. Lindgren, and I. Steinsland. 2014. “Spatial Models with Explanatory Variables in the Dependence Structure.” Spatial Statistics 8: 20–38.
Knorr-Held, L., and H. Rue. 2002. “On Block Updating in Markov Random Field Models for Disease Mapping.” Scandinavian Journal of Statistics 20: 597–614.
Laird, N., and D Olivier. 1981. “Covariance Analysis of Censored Survival Data Using Log-Linear Analysis Techniques.” Journal of the American Statistical Association 76: 231–40.
Lindgren, F. 2012. “Continuous Domain Spatial Models in R-INLA.” The ISBA Bulletin 19 (4): 14–20.
Lindgren, F., and H. Rue. 2015. “Bayesian Spatial and Spatio-Temporal Modelling with R-INLA.” Journal of Statistical Software 63 (19).
Lindgren, F., H. Rue, and J. Lindström. 2011. “An Explicit Link Between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (with Discussion).” J. R. Statist. Soc. B 73 (4): 423–98.
Little, R. J. A., and D. B. Rubin. 2002. Statistical Analysis with Missing Data. 2nd ed. Hoboken, NJ: John Wiley; Sons.
Marshall, E. C., and D. J. Spiegelhalter. 2003. “Approximate Cross-Validatory Predictive Checks in Disease Mapping Models.” Statistics in Medicine 22 (10): 1649–60.
Martino, S., R. Akerkar, and H. Rue. 2010. “Approximate Bayesian Inference for Survival Models.” Scandinavian Journal of Statistics 28 (3): 514–28.
Martins, T. G., D. Simpson, F. Lindgren, and H. Rue. 2013. “Bayesian computing with INLA: New features.” Computational Statistics & Data Analysis 67 (0): 68–83. https://doi.org/10.1016/j.csda.2013.04.014.
Møller, J., A. R. Syversveen, and R. P. Waagepetersen. 1998. “Log Gaussian Cox Processes.” Scandinavian Journal of Statistics 25: 451–82.
Møller, J., and R. P. Waagepetersen. 2003. Statistical Inference and Simulation for Spatial Point Processes. Boca Raton, FL: Chapman & Hall/CRC.
Muff, S., A. Riebler, L. Held, H. Rue, and P. Saner. 2014. “Bayesian Analysis of Measurement Error Models Using Integrated Nested Laplace Approximations.” Journal of the Royal Statistical Society: Series C 64 (2): 231–52.
OpenStreetMap contributors. 2018. “Planet dump retrieved from https://planet.osm.org.” https://www.openstreetmap.org.
Opitz, T., R. Huser, H. Bakka, and H. Rue. 2018. “INLA Goes Extreme: Bayesian Tail Regression for the Estimation of High Spatio-Temporal Quantiles.” Extremes 21 (3): 441–62.
Petris, G., S. Petroni, and P. Campagnoli. 2009. Dynamic Linear Models with R. New York: Springer-Verlag.
Pettit, L. I. 1990. “The Conditional Predictive Ordinate for the Normal Distribution.” Journal of the Royal Statistical Society: Series B (Methodological) 52 (1): 175–84.
Quarteroni, A. M., and A. Valli. 2008. Numerical Approximation of Partial Differential Equations. 2nd ed. New York: Springer.
Ribeiro Jr., P. J., and P. J. Diggle. 2001. “GeoR: A Package for Geostatistical Analysis.” R-NEWS 1 (2): 14–18. http://CRAN.R-project.org/doc/Rnews/.
Rowlingson, B., and P. Diggle. 2017. Splancs: Spatial and Space-Time Point Pattern Analysis. https://CRAN.R-project.org/package=splancs.
Rowlingson, B. S., and P. J. Diggle. 1993. “Splancs: Spatial Point Pattern Analysis Code in S-Plus.” Computers & Geosciences 19 (5): 627–55. https://doi.org/https://doi.org/10.1016/0098-3004(93)90099-Q.
Rozanov, J. A. 1977. “Markov Random Fields and Stochastic Partial Differential Equations.” Math. USSR Sbornik 32 (4)): 515–34. http://dx.doi.org/10.1070/SM1977v032n04ABEH002404.
Rue, H., and L. Held. 2005. Gaussian Markov Random Fields: Theory and Applications. Monographs on Statistics & Applied Probability. Boca Raton, FL: Chapman & Hall.
Rue, H., S. Martino, and N. Chopin. 2009. “Approximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations (with Discussion).” Journal of the Royal Statistical Society: Series B 71 (2): 319–92.
Rue, H., A. I. Riebler, S. H. Sørbye, J. B. Illian, D. P. Simpson, and F. K. Lindgren. 2017. “Bayesian Computing with INLA: A Review.” Annual Review of Statistics and Its Application 4: 395–421.
Rue, H., and H. Tjelmeland. 2002. “Fitting Gaussian Markov Random Fields to Gaussian Fields.” Scandinavian Journal of Statistics 29 (1): 31–49.
Ruiz-Cárdenas, R., E. T. Krainski, and H. Rue. 2012. “Direct Fitting of Dynamic Models Using Integrated Nested Laplace Approximations — INLA.” Computational Statistics & Data Analysis 56 (6): 1808–28. https://doi.org/http://dx.doi.org/10.1016/j.csda.2011.10.024.
Schabenberger, O., and C. A. Gotway. 2004. Statistical Methods for Spatial Data Analysis. Boca Raton, FL: Chapman & Hall/CRC.
Schlather, Martin, Alexander Malinowski, Peter J. Menck, Marco Oesting, and Kirstin Strokorb. 2015. “Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields.” Journal of Statistical Software 63 (8): 1–25. http://www.jstatsoft.org/v63/i08/.
Schmidt, A. M., and A. E. Gelfand. 2003. “A Bayesian Coregionalization Approach for Multivariate Pollutant Data.” Journal of Geographysical Research 108 (D24).
Simpson, D. P., J. B. Illian, F. Lindren, S. H Sørbye, and H. Rue. 2016. “Going Off Grid: Computationally Efficient Inference for Log-Gaussian Cox Processes.” Biometrika 103 (1): 49–70.
Simpson, D. P., H. Rue, A. Riebler, T. G. Martins, and S. H. Sørbye. 2017. “Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors.” Statistical Science 32 (1): 1–28.
Sørbye, S., and H. Rue. 2014. “Scaling Intrinsic Gaussian Markov Random Field Priors in Spatial Modelling.” Spatial Statistics 8: 39–51.
Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. Van der Linde. 2002. “Bayesian Measures of Model Complexity and Fit (with Discussion).” Journal of the Royal Statistical Society: Series B 64 (4): 583–616.
Stephenson, A. G. 2002. “Evd: Extreme Value Distributions.” R News 2 (2): 31–32. https://CRAN.R-project.org/doc/Rnews/.
Taylor, B. M., T. M. Davies, Rowlingson B. S., and P. J. Diggle. 2013. “lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes.” Journal of Statistical Software 52 (4): 1–40. http://www.jstatsoft.org/v52/i04/.
Therneau, T. M. 2015. A Package for Survival Analysis in S. https://CRAN.R-project.org/package=survival.
Therneau, T. M., and P. M. Grambsch. 2000. Modeling Survival Data: Extending the Cox Model. New York: Springer.
Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroid Region.” Economic Geography 2 (46): 234–40.
Vivar, J. C., and M. A. R. Ferreira. 2009. “Spatiotemporal Models for Gaussian Areal Data.” Journal of Computational and Graphical Statistics 18 (3): 658–74.
Wang, X., J. J. Faraway, and Y. Yue Ryan. 2018. Bayesian Regression Modeling with INLA. Boca Raton, FL: Chapman & Hall/CRC.
Watanabe, S. 2013. “A Widely Applicable Bayesian Information Criterion.” Journal of Machine Learning Research 14: 867–97.
West, M., and J. Harrison. 1997. Bayesian Forecasting and Dynamic Models. New York: Springer-Verlag.
Zuur, A. F., E. N. Ieno, and A. A. Saveliev. 2017. Beginner’s Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. Highland Statistics Ltd.