The **geostan**
R package supports a complete spatial analysis workflow with Bayesian
models for areal data, including a suite of functions for visualizing
spatial data and model results. For demonstrations and discussion, see
the package help
pages and vignettes
on spatial autocorrelation and spatial measurement error models.

The package is designed primarily to support public health research
with spatial data; see the **surveil**
R package for time series analysis of public health surveillance
data.

**geostan** is an interface to **Stan**, a state-of-the-art
platform for Bayesian inference.

Statistical models for data recorded across areal units like states, counties, or census tracts.

Incorporate information on data reliability, such as standard errors
of American Community Survey estimates, into any
**geostan** model.

Vital statistics and disease surveillance systems like CDC Wonder
censor case counts that fall below a threshold number;
**geostan** can model disease or mortality risk with
censored observations.

Tools for visualizing and measuring spatial autocorrelation and map patterns, for exploratory analysis and model diagnostics. Visual diagnostics also support the evaluation of survey data quality and observational error models.

Compatible with a suite of high-quality R packages for Bayesian inference and model evaluation.

Tools for building custom spatial models in Stan.

Install **geostan** from CRAN using:

`install.packages("geostan")`

All functions and methods are documented (with examples) on the website reference page. See the package vignettes for more on exploratory spatial data analysis and spatial modeling.

To ask questions, report a bug, or discuss ideas for improvements or new features please visit the issues page, start a discussion, or submit a pull request.

Load the package and the `georgia`

county mortality data
set (ages 55-64, years 2014-2018):

```
library(geostan)
#> This is geostan version 0.2.0
#>
#> Attaching package: 'geostan'
#> The following object is masked from 'package:base':
#>
#> gamma
data(georgia)
```

The `sp_diag`

function provides visual summaries of
spatial data, including a histogram, Moran scatter plot, and map:

```
<- shape2mat(georgia, style = "B")
A sp_diag(georgia$rate.female, georgia, w = A)
#> 3 NA values found in x. They will be dropped from the data before creating the Moran plot. If matrix w was row-standardized, it no longer is. To address this, you can use a binary connectivity matrix, using style = 'B' in shape2mat.
#> Warning: Removed 3 rows containing non-finite values (stat_bin).
```

There are three censored observations in the `georgia`

female mortality data, which means there were 9 or fewer deaths in those
counties. The following code fits a spatial conditional autoregressive
(CAR) model to female county mortality data. By using the
`censor_point`

argument we include our information on the
censored observations to obtain results for all counties:

```
<- prep_car_data(A)
cars #> Range of permissible rho values: -1.661134 1
<- stan_car(deaths.female ~ offset(log(pop.at.risk.female)),
fit censor_point = 9,
data = georgia,
car_parts = cars,
family = poisson(),
cores = 4, # for multi-core processing
refresh = 0) # to silence some printing
#>
#> *Setting prior parameters for intercept
#> Distribution: normal
#> location scale
#> 1 -4.7 5
#>
#> *Setting prior for CAR scale parameter (car_scale)
#> Distribution: student_t
#> df location scale
#> 1 10 0 3
#>
#> *Setting prior for CAR spatial autocorrelation parameter (rho)
#> Distribution: uniform
#> lower upper
#> 1 -1.7 1
```

Passing a fitted model to the `sp_diag`

function will
return a set of diagnostics for spatial models:

```
sp_diag(fit, georgia, w = A)
#> 3 NA values found in x. They will be dropped from the data before creating the Moran plot. If matrix w was row-standardized, it no longer is. To address this, you can use a binary connectivity matrix, using style = 'B' in shape2mat.
#> Warning: Removed 3 rows containing missing values (geom_pointrange).
```

The `print`

method returns a summary of the probability
distributions for model parameters, as well as Markov chain Monte Carlo
(MCMC) diagnostics from Stan (Monte Carlo standard errors of the mean
`se_mean`

, effective sample size `n_eff`

, and the
R-hat statistic `Rhat`

):

```
print(fit)
#> Spatial Model Results
#> Formula: deaths.female ~ offset(log(pop.at.risk.female))
#> Spatial method (outcome): CAR
#> Likelihood function: poisson
#> Link function: log
#> Residual Moran Coefficient: 0.00194825
#> WAIC: 1292.82
#> Observations: 159
#> Data models (ME): none
#> Inference for Stan model: foundation.
#> 4 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> intercept -4.673 0.002 0.090 -4.843 -4.717 -4.676 -4.633 -4.488 1940 1.001
#> car_rho 0.924 0.001 0.059 0.778 0.893 0.937 0.967 0.996 3103 1.000
#> car_scale 0.457 0.001 0.035 0.390 0.433 0.455 0.479 0.531 3176 1.000
#>
#> Samples were drawn using NUTS(diag_e) at Wed Nov 16 14:56:01 2022.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
```

More demonstrations can be found in the package help pages and vignettes.