spdep

R package spdep: Spatial dependence: weighting schemes, statistics and models , A collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial autocorrelation, including global Moran’s I, APLE, Geary’s C, Hubert/Mantel general cross product statistic, Empirical Bayes estimates and Assuncao/Reis Index, Getis/Ord G and multicoloured join count statistics, local Moran’s I and Getis/Ord G, saddlepoint approximations and exact tests for global and local Moran’s I; and functions for estimating spatial simultaneous autoregressive (SAR) lag and error models, impact measures for lag models, weighted and unweighted SAR and CAR spatial regression models, semi-parametric and Moran eigenvector spatial filtering, GM SAR error models, and generalized spatial two stage least squares models. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 34 articles , 1 standard article )

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  1. Cupido, Kyran; Jevtić, Petar; Paez, Antonio: Spatial patterns of mortality in the United States: a spatial filtering approach (2020)
  2. Mihai Tivadar: OasisR: An R Package to Bring Some Order to the World of Segregation Measurement (2019) not zbMATH
  3. Pokhilko, Victoria; Zhang, Qiong; Kang, Lulu; Mays, D’arcy P.: D-optimal design for network A/B testing (2019)
  4. Borcard, Daniel; Gillet, François; Legendre, Pierre: Numerical ecology with R (2018)
  5. Suesse, Thomas: Marginal maximum likelihood estimation of SAR models with missing data (2018)
  6. Suesse, Thomas: Estimation of spatial autoregressive models with measurement error for large data sets (2018)
  7. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  8. Alhamzawi, Rahim: Inference with three-level prior distributions in quantile regression problems (2017)
  9. Barnoy, Eran A.; Kim, Hyun J.; Gjertson, David W.: Complexity in applying spatial analysis to describe heterogeneous air-trapping in thoracic imaging data (2017)
  10. Carracedo, Patricia; Debón, Ana: Spatial statistical tools to assess mortality differences in Europe (2017)
  11. Suesse, Thomas; Zammit-Mangion, Andrew: Computational aspects of the EM algorithm for spatial econometric models with missing data (2017)
  12. Kang, Su Yun; McGree, James; Baade, Peter; Mengersen, Kerrie: A case study for modelling cancer incidence using Bayesian spatio-temporal models (2015)
  13. Nicolas Turenne: svcR: An R Package for Support Vector Clustering improved with Geometric Hashing applied to Lexical Pattern Discovery (2015) arXiv
  14. Patrick Brown: Model-Based Geostatistics the Easy Way (2015) not zbMATH
  15. Roger Bivand; Gianfranco Piras: Comparing Implementations of Estimation Methods for Spatial Econometrics (2015) not zbMATH
  16. Kang, Su Yun; Mcgree, James; Baade, Peter; Mengersen, Kerrie: An investigation of the impact of various geographical scales for the specification of spatial dependence (2014)
  17. Millo, Giovanni: Maximum likelihood estimation of spatially and serially correlated panels with random effects (2014)
  18. Adelchi Azzalini, Giovanna Menardi: Clustering Via Nonparametric Density Estimation: the R Package pdfCluster (2013) arXiv
  19. Duncan Lee: CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors (2013) not zbMATH
  20. Giovanni Millo; Gianfranco Piras: splm: Spatial Panel Data Models in R (2012) not zbMATH

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