| bees | A dataset of bees count data and relevant information in oilseed Brassica fields in an Australian temperate landscape. | 
| ccr | Correct classification rate for predictive models based on cross -validation | 
| cran-comments | Note on notes | 
| datasplit | Split data for k-fold cross-validation | 
| decimaldigit | Digit number after decimal point for a numeric variable | 
| gbmkrigeidwcv | Cross validation, n-fold and leave-one-out for the hybrid methods of generalized boosted regression modeling ('gbm'), 'kriging' and inverse distance weighted ('IDW'). | 
| gbmkrigeidwpred | Generate spatial predictions using the hybrid methods of generalized boosted regression modeling ('gbm'), 'kriging' and inverse distance weighted ('IDW'). | 
| glmcv | Cross validation, n-fold and leave-one-out for generalised linear models ('glm') | 
| glmidwcv | Cross validation, n-fold and leave-one-out for the hybrid method of generalised linear models ('glm') and inverse distance weighted ('IDW') ('glmidw') | 
| glmidwpred | Generate spatial predictions using the hybrid method of generalised linear models ('glm') and inverse distance weighted ('IDW') ('glmidw') | 
| glmkrigecv | Cross validation, n-fold and leave-one-out for the hybrid method of generalised linear models ('glm') and 'krige' ('glmkrige') | 
| glmkrigeidwcv | Cross validation, n-fold and leave-one-out for the hybrid methods of generalised linear models ('glm'), 'kriging' and inverse distance weighted ('IDW'). | 
| glmkrigeidwpred | Generate spatial predictions using the hybrid methods of generalised linear models ('glm'), 'kriging' and inverse distance weighted ('IDW'). | 
| glmkrigepred | Generate spatial predictions using the hybrid method of generalised linear models ('glm') and 'krige' | 
| glmnetcv | Cross validation, n-fold and leave-one-out, for 'glmnet' in 'glmnet' package | 
| glmpred | Generate spatial predictions using generalised linear models ('glm') | 
| glscv | Cross validation, n-fold and leave-one-out for generalized least squares ('gls') | 
| glsidwcv | Cross validation, n-fold and leave-one-out for the hybrid method of generalized least squares ('gls') and inverse distance weighted ('idw') (glsidw) | 
| glsidwpred | Generate spatial predictions using the hybrid method of generalized least squares ('gls') and inverse distance weighted ('IDW') ('glsidw') | 
| glskrigecv | Cross validation, n-fold and leave-one-out for the hybrid method of generalized least squares ('gls') and kriging ('krige') ('glskrige') | 
| glskrigeidwcv | Cross validation, n-fold and leave-one-out for the hybrid methods of generalised least squares ('gls'), 'kriging' and inverse distance weighted ('IDW') | 
| glskrigeidwpred | Generate spatial predictions using the hybrid methods of generalised least squares ('gls'), 'kriging' and inverse distance weighted ('IDW') | 
| glskrigepred | Generate spatial predictions using the hybrid method of generalized least squares ('gls') and kriging ('krige') ('glskrige') | 
| glspred | Generate spatial predictions using generalized least squares ('gls') | 
| krigecv | Cross validation, n-fold and leave-one-out for kriging methods ('krige') | 
| krigepred | Generate spatial predictions using kriging methods ('krige') | 
| rfkrigeidwcv | Cross validation, n-fold and leave-one-out for the hybrid methods of 'random forest' ('RF'), 'kriging' and inverse distance weighted ('IDW') | 
| rfkrigeidwpred | Generate spatial predictions using the hybrid methods of 'random forest' ('RF'), 'kriging' and inverse distance weighted ('IDW'). | 
| sponge2 | A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin | 
| spongelonglat | A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin | 
| svmcv | Cross validation, n-fold and leave-one-out for support vector machine ('svm') | 
| svmidwcv | Cross validation, n-fold and leave-one-out for the hybrid method of support vector machine ('svm') regression and inverse distance weighted ('IDW') (svmidw) | 
| svmidwpred | Generate spatial predictions using the hybrid method of support vector machine ('svm') regression and inverse distance weighted ('IDW') ('svmidw') | 
| svmkrigecv | Cross validation, n-fold and leave-one-out for the hybrid method of support vector machine ('svm') regression and 'krige' (svmkrige) | 
| svmkrigeidwcv | Cross validation, n-fold and leave-one-out for the hybrid methods of support vector machine ('svm') regression , 'kriging' and inverse distance weighted ('IDW'). | 
| svmkrigeidwpred | Generate spatial predictions using the hybrid methods of support vector machine ('svm') regression , 'kriging' and inverse distance weighted ('IDW'). | 
| svmkrigepred | Generate spatial predictions using the hybrid method of support vector machine ('svm') regression and 'krige' (svmkrige) | 
| svmpred | Generate spatial predictions using support vector machine ('svm') | 
| tpscv | Cross validation, n-fold and leave-one-out for thin plate splines ('TPS') |