# spmodel 0.11.1

## Minor Updates

* Added a warning message when `splm()` or `spglm()` converts a non-`POINT` geometry to a `POINT` geometry using `sf::st_centroid()`.
* Updated a warning message when `splm()` or `spglm()` compute distances using a geographic coordinate system (and not a projected coordinate system).

## Bug Fixes

* Fixed a bug in the cloud semivariogram (i.e., `esv(..., cloud = TRUE)`) that incorrectly doubled the semivariance.
* Fix `\textbf`, `\textsf`, and `\textttt` LaTeX rendering issues in vignettes ([#36](https://github.com/USEPA/spmodel/issues/36)).

# spmodel 0.11.0

## Major Updates

* Added support for Block Prediction (i.e., Block Kriging) to estimate averages (and their uncertainties) over a geographic region.
* Added a vignette to the [`spmodel` website](https://usepa.github.io/spmodel/) titled "Block Prediction (i.e., Block Kriging) in spmodel".
* Added a `fc_borders` data set which contains borders for the Four Corners states in the United States.

## Bug Fixes

* Fixed a bug that incorrectly calculated the log determinant of the fixed effects in the restricted log likelihood.
* Fixed a bug that caused an error when `covmatrix(..., cov_type = "pred.pred")` was called on `object` with a non-`NULL` `newdata` element.

# spmodel 0.10.0

## Minor Updates

* Added a robust semivariogram option to `esv()`; see the `robust`argument to `esv()` ([#28](https://github.com/USEPA/spmodel/issues/28)).
* Added `"none"` and `"ie"` spatial covariance types (via `spcov_type` or `spcov_initial`) to `spautor()`, `spgautor()`, and `spautorRF()` ([#27](https://github.com/USEPA/spmodel/issues/27)).
* Fixed the aspect ratio at one for level curve plots of fitted model objects (anisotropic and isotropic).
* Changed the title for level curve plots of fitted model objects from "Anisotropic level curve" to "Isotropic level curve" when the model is isotropic.

# spmodel 0.9.0

## Major Updates

* Added the `range_constrain` argument to `splm()` and `spglm()` to constrain the range parameter to enhance numerical stability. The default for `range_constrain` is `FALSE`, implying the range is not constrained.
* Updated the `seal` data with additional polygons and a factor variable, `stock`, with two levels (`8` and `10`) that indicates seal stock (i.e., seal type).

## Minor Updates

* Changed diagonal tolerance threshold for `spglm()` and `spgautor()` model objects. See [this link](https://usepa.github.io/spmodel/articles/technical.html#sec:computational) for details.
* Added the `"ie"` spatial covariance type to `splm()` and `spglm()` models. For `splm()` models, `"ie"` is an alias for `"none"`. For `spglm()` models, `"none"` now fixes both the `de` and `ie` covariance parameters at zero, while `"ie"` fixes the `de` covariance parameter at zero but allows the `ie` covariance parameter to vary. Thus, `"none"` from `spmodel $\le$ v0.8.0` matches `"ie"` from `spmodel` v0.9.0 and but is different from `"none"` from `spmodel v0.9.0`.
* Added the `na.action` argument to `predict.spmodel()` functions to clarify that missing values in `newdata` return an error.
* Minor documentation updates.

## Bug Fixes

* Fixed a bug that caused incorrect degrees of freedom for the likelihood ratio test (`anova(model1, model2)`) when `estmethod` is `"ml"` for both models.
* Fixed a bug that caused an error in `anova(object1, object2)` when the name of `object1` had special characters (e.g., `$`).

# spmodel 0.8.0

## Major Updates

* Added support for the `emmeans` **R** package for estimating marginal means of `splm()`, `spautor()`, `spglm()`, and `spgautor()` models.
* Added a vignette to the [`spmodel` website](https://usepa.github.io/spmodel/) titled "Using emmeans to Estimate Marginal Means of spmodel Objects".
* Added support for distance-based neighborhood definitions of `spautor()` and `spgautor()` models via the `cutoff` argument, required when `data` are an `sf` object with `POINT` geometry and `W` is not specified.
* Added the `texas` data set, which contains voter turnout data from eligible voters in Texas, USA, during the 1980 Presidential election.
* Added the `lake` and `lake_preds` data sets, which contain data from the United States Environmental Protection Agency's National Lakes Assessment and LakeCat.


## Minor Updates

* Changed the `type` argument in `augment()` for `spglm()` and `spgautor()` models to `type.predict` to match `broom::augment.glm()`.
* `augment()` for `spglm()` and `spgautor()` models now returns fitted values on the link scale by default to match `broom::augment.glm()`.
* Added a `type.residuals` argument for `spglm()` and `spgautor()` models to match `broom::augment.glm()`.
* Updated `logLik()` to match `lm()` and `glm()` behavior. `logLik()` now returns a vector with class `logLik` and attributes `nobs` and `df`.
* Added support for using `AIC()` and `BIC()` from `stats` and removed `spmodel`-specific `AIC()` and `BIC()` methods.
* Added support for `"terms"` prediction for `splm()`, `spautor()`, `spglm()`, and `spgautor()` models.
* Added `scale` and `df` arguments to `predict()` for `splm()` and `spautor()` models.
* Add `dispersion` argument to `predict()` for `spglm()` and `spgautor()` models.
* Enhanced numeric stability of deviance and pseudo R-squared for `spglm()` or `spgautor()` models when `family = "beta"`.
* Added the `cov_type` argument to `covmatrix()` to return observed by observed, prediction by observed, observed by prediction, and prediction by prediction covariance matrices.
* Added a `warning` argument to `glances()` that determines whether relevant warnings should be displayed or not.
* Added a warning message to `glances()` about interpreting likelihood-based statistics (e.g., AIC, AICc, BIC) when a one model has `estmethod = "ml"` and another model has `estmethod = "reml"`.
* Added a warning message to `glances()` about interpreting likelihood-based statistics (e.g., AIC, AICc, BIC) when two models with `estmethod = "reml"` have distinct `formula` arguments.
* Added a warning message to `glances()` about interpreting likelihood-based statistics (e.g., AIC, AICc, BIC) when two models have different sample sizes.
* Added a warning message to `glances()` about interpreting likelihood-based statistics (e.g., AIC, AICc, BIC) when two models have different family supports (which can happen with `spglm()` and `spgautor()` models).
* All data sets now have `tbl_df` and `tbl` classes (i.e., are tibbles).
* Added a `cloud` argument to `esv()` to return a cloud semivariogram.
* `esv()` output now has `tbl_df` and `tbl` classes (i.e., are tibbles) and an `esv` class.
* Added a `plot()` method for `esv` objects.
* Minor vignette updates.
* Minor documentation updates.

# spmodel 0.7.0

## Minor Updates

* Added `AUROC()`  functions to compute the area under the receiver operating characteristic (AUROC) curve for `spglm()` and `spgautor()` models when `family` is `"binomial"` and the response is binary (i.e., represents a single success or failure).
* Added a `BIC()` function to compute the Bayesian Information Criterion (BIC) for `splm()`, `spautor()`, `spglm()`, and `spgautor()` models when `estmethod` is `"reml"` (restricted maximum likelihood; the default) or `"ml"` (maximum likelihood).
* Added a `type` argument to `loocv()` when `cv_predict = TRUE` and using `spglm()` or `spgautor()` models so that predictions may be obtained on the link or response scale.
* Added a warning message when `data` is an `sf` object and a geographic (i.e., degrees) coordinate system is used instead of a projected coordinate system.
* Changed the default behavior of `local` in `predict.spmodel` so that it depends only on the observed data sample size. Now, when the observed data sample size exceeds 10,000 `local` is set to `TRUE` by default. This change was made because prediction for big data depends almost exclusively on the observed data sample size, not the number of predictions desired.
* Minor external data updates (for package testing).
* Minor vignette updates.
* Minor documentation updates.
* Minor error message updates.

## Bug Fixes

* Fixed a bug that prohibited proper indexing when calling `predict()` with the `local` method `"distance"` on a model object fit with a random effect or partition factor.

# spmodel 0.6.0

## Minor Updates

* Improved efficiency of handling random effects in big data models fit using `splm(..., local)` and `spglm(..., local)`.
* Changed `Matrix::rankMatrix(X, method = "tolNorm2")` to `Matrix::rankMatrix(X, method = "qr")` when determining linear independence in `X`, the design matrix of explanatory variables.
* Replaced an error message with a warning message when `X` has perfect collinearities (i.e., is not full rank). If this warning message occurs, it is possible that a subsequent error occurs while model fitting resulting from a covariance matrix that is not positive definite (i.e., a covariance matrix that is singular or computationally singular).
* Improved efficiency of `splm()` when `spcov_type` is `"none"` and there are no random effects [(#15)](https://github.com/USEPA/spmodel/issues/15).
* Added a `range_positive` argument to `spautor()` and `spgautor()` that when `TRUE` (the new default), restricts the range parameter to be positive. When `FALSE` (the prior default), the range parameter may be negative or positive.
* Updated the initial parameter grid search for `spautor()` and `spgautor()` to include range parameter values near the lower and upper boundaries.
* Minor documentation updates

## Bug Fixes

* Fixed a bug that yielded improper predictions when performing local prediction (specifying `local` in a call to `predict(object, newdata, ...)`) when the model object (`object`) was fit using `splm(formula, ...)` or `spglm(formula, ...)` and `formula` contained at least one call to `poly(..., raw = FALSE)`.
* Fixed a bug that caused big data models fit using `splm(..., local)` and `spglm(..., local)` to fail when a user-specified local index was passed to `local` that was a factor variable and at least one factor level not was observed in the local index.
* Fixed a bug that caused models fit using `splm(..., partition_factor)` and `spglm(..., partition_factor)` to fail when the partition factor variable was a factor variable and at least one factor level was not observed in the data.
* Fixed a bug in `spgautor()` that inflated the covariance matrix of the fixed effects (accessible via `vcov()`).
* Fixed a bug in `sp*(spcov_params, ...)` simulation functions that caused an error when `spcov_params` had class `"car"` or `"sar"` and `W` was provided by the user.

# spmodel 0.5.1

## Minor Updates

* Set a default value of `newdata_size = 1` when `newdata_size` was omitted while predicting `type = "response"` for binomial families.
* Improved computational efficiency of `loocv(object)` when `object` was created using `splm()` or `spglm()`, `spcov_type` was `"none"`, and there were no random effects specified via `random`.
* Changed the number of k-means iterations from 10 to 30 (when fitting models using the `local` argument to `splm()` or `spglm()`).
* Added bias and root-mean-squared-prediction error to `loocv(object)`. When `object` was created using `splm()` or `spautor()`, `loocv(object)` added the squared correlation between the observed data and leave-one-out predictions, regarded as a prediction r-squared.
* Improved prediction efficiency (using `predict()` or `augment()`) for `splm()` objects when `spcov_type` was `"none"` and there were no random effects.
* Minor error message updates.

## Bug Fixes

* Fixed a bug that caused local prediction to fail when the fitted model used a partition factor ([#13](https://github.com/USEPA/spmodel/issues/13)).
* Fixed a bug that caused significant increases in computational and memory demands when calling `loocv(object, local, ...)` if `object` was created using `splm(..., random)` or `spglm(..., random)` (i.e., when random effects were specified via the `random` argument to `splm()` or `spglm()`).
* Fixed a bug that caused significant increases in computational and memory demands when calling `loocv(object, local, ...)` if `object` was created using `splm(..., partition_factor)` or `spglm(..., partition_factor)` (i.e., when a partition factor was specified via the `partition_factor` argument to `splm()` or `spglm()`).


# spmodel 0.5.0

## Minor updates

* Predictions can now be made for prediction locations whose random effect levels are not present in the observed data
    * When this occurs, the random-effect covariance between the observed data and these prediction locations is assumed to be zero.
* The default for `local = TRUE` in `splm()` and `spglm()` now uses the `kmeans` assignment method with group sizes approximately equal to 100.
    * Previously, the `random` assignment method was used with group sizes approximately equal to 50.
* The default for `local = TRUE` in `predict()` and `augment()` now uses 100 local neighbors.
    * Previously, 50 local neighbors were used.
* Moved the "A Detailed Guide to `spmodel`" and "Technical Details" vignettes to the package website.
* Added a "Spatial Generalized Linear Models in `spmodel`" vignette to the package website.
* Changed name of "An Overview of Basic Features in `spmodel`" vignette to "An Introduction to `spmodel`" and changed output type from PDF to HTML.
* Other minor vignette updates.
* Minor documentation updates.

## Bug fixes

* Fixed a bug that occurred with prediction for success/failure binomial data (e.g., Bernoulli data) when `local` in `predict()` was `TRUE`.
* Fixed a bug that could affect simulating data using `sprbinom()` when the `size` argument was different from `1`.
* Fixed a bug that could cause local prediction to fail when only one level of a random effect was present in the prediction site's local neighborhood.
* Fixed a bug that could cause an error when local estimation was used for the `"sv-wls"` estimation method.
* Fixed a bug that caused undesirable behavior from `tidy()` when `conf.level` was less than zero or greater than one.

# spmodel 0.4.0

## Major updates

* Added an `spglm()` function to fit spatial generalized linear models for point-referenced data (i.e., generalized geostatistical models).
    * `spglm()` syntax is very similar to `splm()` syntax.
    * Poisson, negative binomial, binomial, beta, gamma, and inverse Gaussian families are accommodated.
    * `spglm()` fitted model objects use the same generics as `splm()` fitted model objects.
* Added an `spgautor()` function to fit spatial generalized linear models for areal data (i.e., spatial generalized autoregressive models).
    * `spgautor()` syntax is very similar to `spautor()` syntax.
    * Poisson, negative binomial, binomial, beta, gamma, and inverse Gaussian families are accommodated.
    * `spgautor()` fitted model objects use the same generics as `spautor()` fitted model objects.

## Minor updates

* In `augment()`, made the `level` and `local` arguments explicit (rather than being passed to `predict()` via `...`).
* Added `offset` support for relevant modeling functions.
* Minor documentation updates.
* Minor vignette updates.

## Bug fixes

* Fixed a bug in `spcov_params()` that yielded output with improper names when a named vector was used as an argument.
* Fixed a bug in `spautor()` that did not properly coerce `M` if given as a matrix (instead of a vector).
* Fixed a bug in `esv()` that prevented coercion of `POLYGON`geometries to `POINT` geometries if `data` was an `sf` object.
* Fixed a bug in `esv()` that did not remove `NA` values from the response.
* Fixed a bug in `splm()` and `spautor()` that caused an error when random effects or partition factors were ordered factors.
* Fixed a bug in `spautor()` that prevented an error from occurring when a partition factor was not categorical or not a factor
* Fixed a bug in `covmatrix(object, newdata)` that returned a matrix with improper dimensions when `spcov_type` was `"none"`.
* Fixed a bug in `predict()` that caused an error when at least one level of a fixed effect factor was not observed within a local neighborhood (when the `local` method was `"covariance"` or `"distance")`.
* Fixed a bug in `cooks.distance()` that used the Pearson residuals instead of the standardized residuals.

# spmodel 0.3.0

## Minor updates

* Added the `varcomp` function to compare variance components.
* Added an error message when there are `NA` values in predictors.
* Added an error message when the design (model) matrix is not invertible (i.e., perfect collinearities are detected).
* Added support for plotting anisotropic level curves of equal correlation when the `which` argument to `plot()` contains `8`.
* Renamed `residuals()` type `raw` to `response` to match `stats::lm()`.
* Changed class of `splm()` output to `"splm"` from `"spmod"` or `"splm_list"` from `"spmod_list"`.
* Changed class of `spautor()` output to `"spautor"` from `"spmod"` or `"spautor_list"` from `"spautor_list"`.
* Changed class of `splmRF()` output to `"splmRF"` from `"spmodRF"` or `"splmRF_list"` from `"spmodRF_list"`.
* Changed class of `spautorRF()` output to `"spautorRF"` from `"spmodRF"` or `"spautorRF_list"` from `"spmodRF_list"`.
* Methods corresponding to a generic function defined outside of `spmodel` are now all documented using
    an `.spmodel` suffix, making it easier to find documentation of a particular
    `spmodel` method for the generic function of interest.
* Added an error when random effect grouping variables or partition factors are numeric.
* Added an error when random effect or partition factor levels in `newdata` are not also in `data`.
* Updated citation information.

## Bug fixes

* Fixed a bug that produced irregular spacing in an error message for `spcov_initial()`.
* Fixed a bug that prevented proper display of row names when calling `predict()`
    with `interval = "confidence"`.
* Fixed a bug that sometimes caused miscalculations in model-fitting and prediction
    when random effect or partition factor variables were improperly coerced to a different type.
* Fixed bugs that sometimes caused miscalculations in certain model diagnostics.
* Fixed inconsistencies in several non-exported generic functions.
* Fixed a bug that prevented names from appearing with output from certain model diagnostics.

# spmodel 0.2.0

* `spmodel` v0.3.0 changed the names of `spmod`, `spmodRF`, `spmod_list`, and `spmodRF_list` objects.

## Minor updates

* `splm()` and `spautor()` allow multiple models to be fit when the `spcov_type` argument is a vector of length greater than one or the `spcov_initial` argument is a list (with length greater than one) of `spcov_initial` objects.
    * The resulting object is a list with class `spmod_list`. Each element of the list holds a different model fit.
    * `glances()` is used on an `spmod_list` object to glance at each model fit.
    * `predict()` is used on an `spmod_list` object to predict at the locations in `newdata` for each model fit.
* Added the `splmRF()` and `spautorRF()` functions to fit random forest spatial residual models.
    * The resulting object has class `spmodRF` (one spatial covariance) or `spmodRF_list` (multiple spatial covariances)
    * These objects are built for use with `predict()` to perform prediction.
* Added the `covmatrix()` function to extract covariance matrices from an `spmod` object fit using `splm()` or `spautor()`.
* Minor vignette updates.
* Minor documentation updates.

## Bug fixes

* Fixed a bug that prevents display of spatial covariance type in summary of `spmod` objects.
* Fixed a bug that prevented prediction of factor variables when all levels of all factor variables did not appear in `newdata`.

# spmodel 0.1.1

## Minor updates

* Updated unit tests so that they are compatible with an upcoming version of `Matrix`.

# spmodel 0.1.0

This is the initial release of spmodel.
