Function reference • geostan

Package overview
`geostan-package`	The geostan R package.
Spatial analysis Functions for measuring and visualizing spatial autocorrelation and dispersion, including model diagnostics
`aple()`	Spatial autocorrelation estimator
`expected_mc()`	Expected value of the residual Moran coefficient
`gr()`	The Geary Ratio
`lg()`	Local Geary
`lisa()`	Local Moran's I
`mc()`	The Moran coefficient
`me_diag()`	Data model diagnostics
`moran_plot()`	Moran plot
`n_eff()`	Effective sample size
`sp_diag()`	Spatial data diagnostics
`row_standardize()`	Row-standardize a matrix; safe for zero row-sums.
Models Model fitting functions and methods
`stan_car()`	Conditional autoregressive (CAR) models
`stan_esf()`	Spatial filtering
`stan_glm()`	Generalized linear models
`stan_icar()`	Intrinsic autoregressive models
`stan_sar()`	Simultaneous autoregressive (SAR) models
`predict(<geostan_fit>)`	Predict method for `geostan_fit` models
`print(<geostan_fit>)` `plot(<geostan_fit>)`	print or plot a fitted geostan model
`residuals(<geostan_fit>)` `fitted(<geostan_fit>)` `spatial()`	Extract residuals, fitted values, or the spatial trend
`as.matrix(<geostan_fit>)` `as.data.frame(<geostan_fit>)` `as.array(<geostan_fit>)`	Extract samples from a fitted model
`sp_diag()`	Spatial data diagnostics
`posterior_predict()`	Draw samples from the posterior predictive distribution
`uniform()` `normal()` `student_t()` `gamma()` `hs()`	Prior distributions
Convenience functions Tools for working with spatial data and geostan models
`auto_gaussian()`	Auto-Gaussian family for CAR models
`edges()`	Edge list
`get_shp()`	Download shapefiles
`make_EV()`	Extract eigenfunctions of a connectivity matrix for spatial filtering
`prep_car_data()`	Prepare data for a Stan CAR model
`prep_icar_data()`	Prepare data for ICAR models
`prep_me_data()`	Prepare data for spatial measurement error models
`prep_sar_data()`	Prepare data for a simultaneous autoregressive (SAR) model
`se_log()`	Standard error of log(x)
`shape2mat()`	Create spatial and space-time connectivity matrices
`sim_sar()`	Simulate spatially autocorrelated data
`uniform()` `normal()` `student_t()` `gamma()` `hs()`	Prior distributions
`waic()`	Widely Applicable Information Criteria (WAIC)
Data
`georgia`	Georgia all-cause, sex-specific mortality, ages 55-64, years 2014-2018
`sentencing`	Florida state prison sentencing counts by county, 1905-1910

Reference

Package overview

Spatial analysis

Models

Convenience functions

Data