NEWS.md
SSN2 website titled “Adding Explanatory Variables to ‘SSN’ Objects Directly in R Using StreamCat”.SSN2 website titled “Fitting ‘SSN’ Models to Large Data Sets and Making Predictions (i.e., Kriging)”size argument to the local argument in ssn_lm() and ssn_glm() from 100 to 200. This enhances the approximation’s accuracy but can slightly increase computational complexity.size argument to the local argument in predict() and augment() from 2000 to 4000. This enhances the approximation’s accuracy but can slightly increase computational complexity.emmeans R package for estimating marginal means of ssn_lm() and ssn_glm() models.ssn_create_bigdist() function was added to create large distance matrices using the filematrix R package. Estimation for large data sets is performed by leveraging the local argument to ssn_lm() and ssn_glm(). Prediction for large data sets is performed by leveraging the local argument to predict() (and augment()). When local is used, SSN2 looks for distance matrices created using ssn_create_bigdist().ssn_import() so that it does not force an overwrite of the netgeom column when it already exists.verbose argument to ssn_import(), ssn_import_predpts(), and createBinaryID() to control whether warning messages are printed to the R console.na.action argument to predict.ssn_lm() and predict.ssn_glm() functions to clarify that missing values in newdata return an error.type argument in augment() for ssn_glm() models to type.predict to match broom::augment.glm().augment() for ssn_glm() models now returns fitted values on the link scale by default to match broom::augment.glm().type.residuals argument for ssn_glm() models to match broom::augment.glm().logLik() to match lm() and glm() behavior. logLik() now returns a vector with class logLik and attributes nobs and df.AIC() and BIC() from stats and removed SSN2-specific AIC() methods.warning argument to glances() that determines whether relevant warnings should be displayed or not.glances() about interpreting likelihood-based statistics (e.g., AIC, AICc, BIC) when a one model has estmethod = "ml" and another model has estmethod = "reml".glances() about interpreting likelihood-based statistics (e.g., AIC, AICc, BIC) when two models with estmethod = "reml" have distinct formula arguments.glances() about interpreting likelihood-based statistics (e.g., AIC, AICc, BIC) when two models have different sample sizes.glances() about interpreting likelihood-based statistics (e.g., AIC, AICc, BIC) when two models have different family supports (which can happen with ssn_glm() models).cloud argument to Torgegram() to return a cloud Torgegram.cex to plot.Torgegram().Torgegram(); see the robustargument to Torgegram()
AUROC() function to compute the area under the receiver operating characteristic (AUROC) curve for ssn_glm models when family is "binomial" and the response is binary (i.e., represents a single success or failure).type argument to loocv() when cv_predict = TRUE and using ssn_glm() models so that predictions may be obtained on the link or response scale."terms" prediction for ssn_lm() and ssn_glm() models.scale and df arguments to predict() for ssn_lm() models.dispersion argument to predict() for ssn_glm() models.anova(model1, model2)) when estmethod is "ml" for both models #25.anova(object1, object2) when the name of object1 had special characters (e.g., $).README.md) updates as part of a submission to Journal of Open Source Software. Relevant issues associated with the review are available at #11, #12, #13, #14, #15, #16, #17, #20, #21. The review is linked here..ssn folder that is accessed when importing SSN objects via ssn_import().ssn_names() to return column names in the edges, obs, and preds elements of an SSN object.Matrix::rankMatrix(X, method = "tolNorm2") to Matrix::rankMatrix(X, method = "qr") to enhance stability when determining linear independence in X, the design matrix of explanatory variables.X has perfect collinearities (i.e., is not full rank).format_additive argument from ssn_import() because of transition to geopackage support, which eliminates the need to convert additive function values to text.create_netgeom() function to create the network geometry column for the edges, obs, and preds elements in an SSN object.SSN_to_SSN2() that caused an error using ssn_write() with no prediction sites.names.SSN() with ssn_names(), as names.SSN() prevented proper naming of elements in the SSN object.netgeometry to netgeom to avoid exceeding the 10 character limit for column/field names while writing to shapefiles (#2).family is missing in ssn_glm() (#8).SSN_to_SSN2().Torgegram() that prevented intended computation when cutoff was specified.plot.Torgegram() that occasionally prevented proper spacing of the legend.ssn_glm() model objects (and their summaries) when all covariance parameters were known.euclid_type was "none".taildown_type was "spherical".ssn_glm() objects.