geostan 0.4.1

Minor changes

geostan 0.4.0

New Additions

SAR models

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.

Minor changes

geostan 0.3.0

New additions

Minor changes

geostan 0.2.1

Minor changes

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 0.2.0

Major changes

Models for censored disease and mortality data

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).

Measurement error models improved

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

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.

Minor changes

A predict method for marginal effects

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.

Centering covariates with measurement error models

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.

geostan 0.1.1

Minor changes

geostan 0.1.0

geostan’s first release.