Man pages for brandon-mosqueda/SKM
Sparse Kernels Methods
| accuracy | Accuracy |
| bayesian_model | Fit a Bayesian Generalized Linear Regression Model (BGLR) |
| BayesianOptimization | Bayesian Optimization |
| best_lines_match | Percentage of best lines present in the predictions |
| brier_score | Brier Score |
| categorical_summary | Categorical summary |
| cholesky | Compute Cholesky |
| cholesky_no_definite | Cholesky |
| coef.Model | Model coefficients |
| confusion_matrix | Confusion matrix |
| cv_kfold | K-fold cross validation folds generation |
| cv_kfold_strata | Stratified K-fold cross validation folds generation |
| cv_leve_one_group_out | Leave one group out cross validation folds generation |
| cv_na | NA cross validation folds generation |
| cv_one_env_out | Leave one environment out cross validation folds generation |
| cv_random | Random cross validation folds generation |
| cv_random_line | Random line cross validation folds generation |
| cv_random_strata | Stratified random cross validation folds generation |
| deep_learning | Fit a Deep Learning Model |
| echo | Print a message in the console. |
| f1_score | F1 score |
| generalized_boosted_machine | Fit a Generalized Boosted Machine (GBM) |
| generalized_linear_model | Fit a Penalized Generalized Linear Model |
| gs_bayesian | Bayesian Cross Validation for Genomic Selection |
| gs_fast_bayesian | Fast Bayesian Cross Validation for Genomic Selection |
| gs_summaries | Summaries for Genomic Selection |
| hush | Hide code output |
| is_empty | Is an empty object. |
| kappa_coeff | Cohen's Kappa coefficient |
| kernelize | Apply a kernel |
| KFold | K-Folds cross validation index generator |
| maape | Mean Arctangent Absolute Percentage Error |
| mae | Mean Abosolute Error |
| Maize | Genomic Maize data. |
| math_mode | Mathematical Mode |
| Matrix_runif | Matrix runif |
| matthews_coeff | Matthews Correlation Coefficient (MCC) |
| Min_Max_Inverse_Scale_Vec | MinMax Inverse Scaling |
| Min_Max_Scale_Mat | Matrix MinMax Scaling |
| mixed_model | Fit a Mixed Model (lme4GS) |
| mkdir | Make directory |
| mse | Mean Squared Error |
| ndcg | Normalized Discounted Cumulative Gain (NDCG) |
| nonull | nonull |
| nrmse | Normalized Root Mean Squared Error |
| numeric_summary | Numeric summary |
| partial_least_squares | Fit a Partial Least Squares Regression Model (PLSR) |
| pccc | Proportion of Correctly Classified Cases (PCCC) |
| pcic | Proportion of Incorrectly Classified Cases |
| pearson | Pearson's correlation coefficient |
| pr_auc | Precision-Recall Area Under the Curver (PR-AUC) |
| precision | Precision |
| predict.BayesianModel | Predict Bayesian model |
| predict.MixedModel | Predict Mixed model |
| predict.Model | Predict model |
| predict.PartialLeastSquaresModel | Predict Partial Least Squares model |
| r2 | R-Squared |
| random_forest | Fit a Random Forest Model |
| recall | Recall |
| rmse | Root Mean Squared Error |
| roc_auc | ROC Area Under the Curver (ROC-AUC) |
| sensitivity | Sensitivity |
| spearman | Spearman's correlation coefficient |
| specificity | Specificity |
| support_vector_machine | Fit a Support Vector Machine (SVM) |
| to_data_frame | Convert data to data.frame |
| to_matrix | Convert data to matrix |
| Utility | Utility Computing Function |
| Utility_Max | Utility Maximization Function |
| Wheat | Genomic Wheat data. |
| write_csv | Write a CSV |
