spatialreg1.3-5 package

Spatial Regression Analysis

Approximate profile-likelihood estimator (APLE) permutation test

Approximate profile-likelihood estimator (APLE) scatterplot

Approximate profile-likelihood estimator (APLE)

Spatial regression model Jacobian computations

Spatial weights matrix eigenvalues

Spatial simultaneous autoregressive error model estimation by GMM

Spatial simultaneous autoregressive SAC model estimation by GMM

Impacts in spatial lag models

Compute SAR generating operator

Matrix exponential spatial lag model

Find extreme eigenvalues of binary symmetric spatial weights

MCMC sample from fitted spatial regression

Moran eigenvector GLM filtering

Spatial simultaneous autoregressive model estimation by maximum likeli...

Prediction for spatial simultaneous autoregressive linear model object...

Likelihood ratio test

Bayesian MCMC spatial simultaneous autoregressive model estimation

Options for parallel support

Control checking of spatial object IDs

Create symmetric similar weights lists

Spatial Durbin linear (SLX, spatially lagged X) model

Spatial neighbour sparse representation

Semi-parametric spatial filtering

Spatial conditional and simultaneous autoregression model estimation

Generalized spatial two stage least squares

Spatial weights matrix powers traces

A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) <doi:10.3390/math9111276>. 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'.

Maintainer: Roger Bivand License: GPL-2 Last published: 2024-08-19

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