Bayesian Spatial Analysis
Spatial autocorrelation estimator
Auto-Gaussian family for CAR models
Edge list
Eigenvalues of a spatial weights matrix: for spatial regression with r...
Expected value of the residual Moran coefficient
The geostan R package.
Download shapefiles
The Geary Ratio
Local Geary
Local Moran's I
Extract log-likelihood
Extract eigenfunctions of a connectivity matrix for spatial filtering
The Moran coefficient (Moran's I)
Measurement error model diagnostics
Moran scatter plot
Effective sample size
Count neighbors in a connectivity matrix
Draw samples from the posterior predictive distribution
Predict method for geostan_fit models
Prepare data for the CAR model
Prepare data for the CAR model: raster analysis
Prepare data for ICAR models
Prepare data for spatial measurement error models
Prepare data for a simultaneous autoregressive (SAR) model
Prepare data for SAR model: raster analysis
print or plot a fitted geostan model
Prior distributions
Extract residuals, fitted values, or the spatial trend
Row-standardize a matrix; safe for zero row-sums.
Extract samples from a fitted model
Standard error of log(x)
Create spatial and space-time connectivity matrices
Simulate spatially autocorrelated data
Visual displays of spatial data and spatial models
Conditional autoregressive (CAR) models
Spatial filtering
Generalized linear models
Intrinsic autoregressive models
Simultaneous autoregressive (SAR) models
Model comparison
For spatial data analysis; provides exploratory spatial analysis tools, spatial regression models, disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health surveillance data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
Useful links