- The recommended citation for the software has been updated since the
software has gone through peer-review in
*The Journal of Open Source Software*. Many thanks to the two peer reviewers of the project, Chris Jochem and Virgilio Gómez Rubio. The following changes were introduced following Chris J.’s recommendations. - The spatial diagonstic function (
`sp_diag`

) will now take a spatial connectivity matrix from the fitted model object provided by the user. This way the matrix will be the same one that was used to fit the model. (All of the model fitting functions have been updated to support this functionality.) - The documentation of the methods for fitted models
(
`residuals`

,`fitted`

,`spatial`

, etc.) were previously packed into one page. Now, the documentation is spread over a few pages and the methods are grouped together in a more reasonable fashion.

The simultaneously-specified spatial autoregressive (SAR)
model—referred to as the spatial error model (SEM) in the spatial
econometrics literature—has been implemented. The SAR model can be
applied directly to continuous data (as the likelihood function) or it
can be used as prior model for spatially autocorrelated parameters.
Details are provided on the documentation page for the
`stan_sar`

function.

Previously, when getting fitted values from an auto-normal model (i.e., the CAR model with

`family = auto_gaussian()`

) the fitted values did not include the implicit spatial trend. Now, the`fitted.geostan_fit`

method will return the fitted values with the implicit spatial trend; this is consistent with the behavior of`residuals.geostan_fit`

, which has an option to`detrend`

the residuals. This applies to the SAR and CAR auto-normal specifications. For details, see the documentation pages for`stan_car`

and`stan_sar`

.The documentation for the models (

`stan_glm`

,`stan_car`

,`stan_esf`

,`stan_icar`

,`stan_sar`

) now uses Latex to typeset the model equations.

- New exploratory spatial data analysis functions have been added: the Geary Ratio (GR) and the local Geary’s C. These complement the Moran coefficient and local Moran’s I.
- The vignette on spatial autocorrelation has been updated and expanded, including with a short discussion of exploratory spatial data analysis (ESDA).
- The vignette on spatial measurement error models/working with ACS data has been completely re-written.

- geostan models can now be used with the bridgesampling package for
model comparison with Bayes factors (e.g., use
`bridge_sampler(geostan_fit$stanfit)`

). By default, geostan only collects MCMC samples for parameters that are expected to be of some interest for users. To become compatible with bridgesampling, the`keep_all`

argument was added to all of the model fitting functions. For important background and details see the bridgesampling package documentation and vignettes on CRAN. - stan_car now has an option to provide the connectivity matrix C, which is used to calculate spatial-lag of X (SLX) terms and residual spatial autocorrelation. Previously, there was no option to provide this matrix, as it was taken from the car_parts argument. However, that choice is only appropriate when the WCAR specification is used. Now, if C is missing and the WCAR specification has not been used a warning will appear.
- Previously, the
`lisa`

function would automatically center and scale the variate before computing local Moran’s I. Now, the variate will be centered and scaled by default but the user has the option to turn the scaling off (so the variate will be centered, but not divided by its standard deviation). This function also row-standardized the spatial weights matrix automatically, but there was no reason why. That’s not done anymore.

The distance-based CAR models that are prepared by the
`prep_car_data`

function have changed slightly. The
conditional variances were previously a function of the sum of
neighboring inverse distances (in keeping with the specification of the
connectivity matrix); this can lead to very skewed frequency
distributions of the conditional variances. Now, the conditional
variances are equal to the inverse of the number of neighboring sites.
This is in keeping with the more common CAR model specifications.

geostan now supports Poisson models with censored count data, a
common problem in public health research where small area disease and
mortality counts are censored below a threshold value. Model for
censored outcome data can now be implemented using the
`censor_point`

argument found in all of the model fitting
functions (stan_glm, stan_car, stan_esf, stan_icar).

The measurement error models have been updated in three important respects:

- There is now a prep_me_data function which must be used to create
the list of data for the ME models. See
`?prep_me_data`

. - For covariates that are proportions or rates, the ME models now have
an option for using a logit transformation on the variable. Again, see
`?prep_me_data`

for usage. - Previously, when using
`stan_car`

, ME models automatically employed the CAR model as a prior for the modeled covariates. That has changed, so that the default behavior for the ME models is the same across all`stan_*`

models (CAR, GLM, ESF, ICAR).

The second change listed above is particularly useful for variables
that are highly skewed, such as the poverty rate. To determine whether a
transformation should be considered, it can be helpful to evaluate
results of the ME model (with the untransformed covariate) using the
`me_diag`

function. The logit transform is done on the
‘latent’ (modeled) variable, not the raw covariate. This transformation
cannot be applied to the raw data by the user because that would require
the standard errors of covariate estimates (e.g., ACS standard errors)
to be adjusted for the transformation.

A `predict`

method has been introduced for fitted geostan
models; this is designed for calculating marginal effects. Fitted values
of the model are still returned using `fitted`

and the
posterior predictive distribution is still accessible via
`posterior_predict`

.

The `centerx`

argument has been updated to handle
measurement error models for covariates. The centering now happens
inside the Stan model so that the means of the modeled covariates
(latent variables) are used instead of the raw data mean.

- The stan files for the CAR model have been combined with the ‘foundation.stan’ file, which compresses the file size considerably.
- The vignette on spatial autocorrelation has also been updated to include model diagnostics.
- A new example has been added to the stan_car documentation.

geostan’s first release.