The `gamma`

function (which is available to help set prior distributions) has been renamed to `geostan::gamma2`

to avoid conflict with `base::gamma`

.

Some code for `geostan::stan_car`

was cleaned up to avoid sending duplicate variables to the Stan model when a spatial ME (measurement error) model was used: https://github.com/ConnorDonegan/geostan/issues/17. This should not change any functionality and there is no reason to suspect that results were ever impacted by the duplicate variables.

This release was built using rstan 2.26.23, which incorporates Stan’s new syntax for declaring arrays. Some models seems to run a little bit faster, but otherwise there are no changes that users should notice.

The warnings issued about the sp package can be ignored; these are due to geostan’s dependence on spdep, which imports sp but does not use any of the deprecated functions.

A new vignette shows how to implement some of geostan’s spatial models directly in Stan, using the custom Stan functions that make the CAR and SAR models sample quickly, and using some geostan functions that make the data cleaning part easy.

This release fixes some issues that were introduced with the `slim`

and `drop`

arguments (in v0.5.0).

The package now provides some support for spatial regression with raster data, including for layers with hundreds of thousands of observations (possibly more, depending on one’s computational resources). Two new additions make this possible.

`slim = TRUE`

The model fitting functions (`stan_glm`

,`stan_car`

,`stan_sar`

,`stan_esf`

,`stan_icar`

) now provide the option to trim down the parameters for which MCMC samples are collected. For large N and/or many N-length vectors of parameters, this option can speed up sampling considerably and reduce memory usage. The new`drop`

argument provides users control over which parameter vectors will be ignored. This functionality may be helpful for any number of purposes, including modeling large data sets, measurement error models, and Monte Carlo studies.`prep_sar_data2`

and`prep_car_data2`

These two functions can quickly prepare required data for SAR and CAR models when using raster layers (observations on a regularly spaced grid). The standard and more generally applicable functions`prep_car_data`

and`prep_sar_data`

are limited in terms of the size of spatial weights matrices they can handle.

These new functions are dicussed in a new vignette titled “Raster regression.”

The PDF documentation has been improved—previously, multi-line equations were not rendered properly. Now they render correctly, and a mistake in the description of Binomial CAR models has been corrected.

- 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.