| any_constant | Check for constant columns |
| as.data.tree.linadleaves | Convert 'linadleaves' to 'data.tree' object |
| as.data.tree.rpart | Convert 'rpart' rules to 'data.tree' object |
| as.data.tree.shyoptleaves | Convert 'shyoptleaves' to 'data.tree' object |
| auc | Area under the ROC Curve |
| auc_pairs | Area under the Curve by pairwise concordance |
| bacc | Balanced Accuracy |
| betas.lihad | Extract coefficients from Additive Tree leaves |
| bias_variance | Bias-Variance Decomposition |
| binmat2vec | Binary matrix times character vector |
| boost | Boost an 'rtemis' learner for regression |
| bootstrap | Bootstrap Resampling |
| brier_score | Brier Score |
| calibrate | Calibrate predicted probabilities using GAM |
| calibrate_cv | Calibrate cross-validated model |
| cartLiteBoostTV | Boost an 'rtemis' learner for regression |
| catrange | Print range of continuous variable |
| catsize | Print Size |
| c_CMeans | Fuzzy C-means Clustering |
| c_DBSCAN | Density-based spatial clustering of applications with noise |
| c_EMC | Expectation Maximization Clustering |
| c_H2OKMeans | K-Means Clustering with H2O |
| c_HARDCL | Clustering by Hard Competitive Learning |
| check_data | Check Data |
| check_files | Check file(s) exist |
| checkpoint_earlystop | Early stopping check |
| chill | Chill |
| c_HOPACH | Hierarchical Ordered Partitioning and Collapsing Hybrid |
| c_KMeans | K-means Clustering |
| class_error | Classification Error |
| class_imbalance | Class Imbalance |
| clean_colnames | Clean column names |
| clean_names | Clean names |
| clust | Clustering with 'rtemis' |
| c_MeanShift | Mean Shift Clustering |
| c_NGAS | Neural Gas Clustering |
| coef.lihad | Extract coefficients from Hybrid Additive Tree leaves |
| col2grayscale | Color to Grayscale |
| col2hex | Convert R color to hexadecimal code |
| colMax | Collapse data.frame to vector by getting column max |
| colorAdjust | Adjust HSV Color |
| color_fade | Fade color towards target |
| colorGrad | Color Gradient |
| colorgradient.x | Color gradient for continuous variable |
| colorGrad.x | Color gradient for continuous variable |
| color_invertRGB | Invert Color in RGB space |
| color_mean | Average colors |
| colorMix | Create an alternating sequence of graded colors |
| colorOp | Simple Color Operations |
| color_order | Order colors |
| color_separate | Separate colors |
| color_sqdist | Squared Color Distance |
| cols2list | Convert data frame columns to list elements |
| c_PAM | Partitioning Around Medoids |
| c_PAMK | Partitioning Around Medoids with k Estimation |
| create_config | Create rtemis configuration file |
| crules | Combine rules |
| c_SPEC | Spectral Clustering |
| dat2bsplinemat | B-Spline matrix from dataset |
| dat2poly | Create n-degree polynomial from data frame |
| date2factor | Date to factor time bin |
| date2ym | Date to year-month factor |
| date2yq | Date to year-quarter factor |
| ddb_collect | Collect a lazy-read duckdb table |
| ddb_data | Read CSV using DuckDB |
| ddSci | Format Numbers for Printing |
| decom | Matrix Decomposition with 'rtemis' |
| dependency_check | 'rtemis' internal: Dependencies check |
| desaturate | Pastelify a color (make a color more pastel) |
| describe | Describe generic |
| df_movecolumn | Move data frame column |
| d_H2OAE | Autoencoder using H2O |
| d_H2OGLRM | Generalized Low-Rank Models (GLRM) on H2O |
| d_ICA | Independent Component Analysis |
| d_Isomap | Isomap |
| distillTreeRules | Distill rules from trained RF and GBM learners |
| d_KPCA | Kernel Principal Component Analysis |
| d_LLE | Locally Linear Embedding |
| d_MDS | Multidimensional Scaling |
| d_NMF | Non-negative Matrix Factorization (NMF) |
| d_PCA | Principal Component Analysis |
| dplot3_addtree | Plot AddTree trees |
| dplot3_bar | Interactive Barplots |
| dplot3_box | Interactive Boxplots & Violin plots |
| dplot3_calibration | Draw calibration plot |
| dplot3_cart | Plot 'rpart' decision trees |
| dplot3_conf | Plot confusion matrix |
| dplot3_fit | True vs. Predicted Plot |
| dplot3_graphd3 | Plot graph using 'networkD3' |
| dplot3_graphjs | Plot network using 'threejs::graphjs' |
| dplot3_heatmap | Interactive Heatmaps |
| dplot3_leaflet | Plot interactive choropleth map using 'leaflet' |
| dplot3_linad | Plot a Linear Additive Tree trained by s_LINAD using... |
| dplot3_pie | Interactive Pie Chart |
| dplot3_protein | Plot the amino acid sequence with annotations |
| dplot3_pvals | Barplot p-values using dplot3_bar |
| dplot3_spectrogram | Interactive Spectrogram |
| dplot3_table | Simple HTML table |
| dplot3_ts | Interactive Timeseries Plots |
| dplot3_varimp | Interactive Variable Importance Plot |
| dplot3_volcano | Volcano Plot |
| dplot3_x | Interactive Univariate Plots |
| dplot3_xt | Plot timeseries data |
| dplot3_xy | Interactive Scatter Plots |
| dplot3_xyz | Interactive 3D Plots |
| drange | Set Dynamic Range |
| d_SPCA | Sparse Principal Component Analysis |
| d_SVD | Singular Value Decomposition |
| dt_check_unique | Check if all levels in a column are unique |
| dt_describe | Describe data.table |
| dt_get_column_attr | Tabulate column attributes |
| dt_get_duplicates | Get index of duplicate values |
| dt_get_factor_levels | Get factor levels from data.table |
| dt_index_attr | Index columns by attribute name & value |
| dt_inspect_type | Inspect column types |
| dt_keybin_reshape | Long to wide key-value reshaping |
| dt_merge | Merge data.tables |
| dt_names_by_attr | List column names by attribute |
| dt_names_by_class | List column names by class |
| dt_pctmatch | Get N and percent match of values between two columns of two... |
| dt_pctmissing | Get percent of missing values from every column |
| dt_set_autotypes | Set column types automatically |
| dt_set_clean_all | Clean column names and factor levels in-place |
| dt_set_cleanfactorlevels | Clean factor levels of data.table in-place |
| dt_set_logical2factor | Convert data.table logical columns to factor with custom... |
| d_TSNE | t-distributed Stochastic Neighbor Embedding |
| d_UMAP | Uniform Manifold Approximation and Projection (UMAP) |
| earlystop | Early stopping |
| error | Error functions |
| expand.boost | Expand boosting series |
| explain | Explain individual-level model predictions |
| f1 | F1 score |
| factor_harmonize | Factor harmonize |
| factor_NA2missing | Factor NA to "missing" level |
| factoryze | Factor Analysis |
| fct_describe | Decribe factor |
| format.call | Format method for 'call' objects |
| formatLightRules | Format LightRuleFit rules |
| formatRules | Format rules |
| fwhm2sigma | FWHM to Sigma |
| get_loaded_pkg_version | Get version of all loaded packages (namespaces) |
| get_mode | Get the mode of a factor or integer |
| getnames | Get names by string matching |
| get-names | Get factor/numeric/logical/character names from... |
| getnamesandtypes | Get data.frame names and types |
| get_rules | Get RuleFit rules |
| get_vars_from_rules | Extract variable names from rules |
| ggtheme_dark | 'rtemis' 'ggplot2' dark theme |
| ggtheme_light | 'rtemis' 'ggplot2' light theme |
| glmLite | Bare bones decision tree derived from 'rpart' |
| gmean | Geometric mean |
| gp | Bayesian Gaussian Processes [R] |
| grapes-BC-grapes | Binary matrix times character vector |
| graph_node_metrics | Node-wise (i.e. vertex-wise) graph metrics |
| gridCheck | 'rtemis' internal: Grid check |
| gtTable | Greater-than Table |
| htest | Basic Bivariate Hypothesis Testing and Plotting |
| inherits_check | Test class of object |
| inherits_test | Check class of object |
| inspect_type | Inspect character and factor vector |
| invlogit | Inverse Logit |
| is_check | Check type of object |
| is_constant | Check if vector is constant |
| is_discrete | Check if variable is discrete (factor or integer) |
| is_test | Test type of object |
| kfold | K-fold Resampling |
| labelify | Format text for label printing |
| lincoef | Linear Model Coefficients |
| list2csv | Write list elements to CSV files |
| logistic | Logistic function |
| logit | Logit transform |
| logloss | Log Loss for a binary classifier |
| loocv | Leave-one-out Resampling |
| lotri2edgeList | Connectivity Matrix to Edge List |
| lsapply | 'lsapply' |
| make_key | Make key from data.table id - description columns |
| massGAM | Mass-univariate GAM Analysis |
| massGLAM | Mass-univariate GLM Analysis |
| massGLM | Mass-univariate GLM Analysis |
| massUni | Mass-univariate Analysis |
| matchcases | Match cases by covariates |
| mergelongtreatment | Merge panel data treatment and outcome data |
| meta_mod | Meta Models for Regression (Model Stacking) |
| mgetnames | Get names by string matching multiple patterns |
| mhist | Histograms |
| mlegend | Add legend to 'mplot3' plot |
| mod_error | Error Metrics for Supervised Learning |
| mplot3_adsr | 'mplot3': ADSR Plot |
| mplot3_bar | 'mplot3': Barplot |
| mplot3_box | 'mplot3': Boxplot |
| mplot3_conf | Plot confusion matrix |
| mplot3_confbin | Plot extended confusion matrix for binary classification |
| mplot3_decision | 'mplot3': Decision boundaries |
| mplot3_fit | True vs. Fitted plot |
| mplot3_fret | 'mplot3': Guitar Fretboard |
| mplot3_graph | Plot 'igraph' networks |
| mplot3_harmonograph | Plot a harmonograph |
| mplot3_heatmap | 'mplot3' Heatmap ('image'; modified 'heatmap') |
| mplot3_img | Draw image (False color 2D) |
| mplot3_laterality | Laterality scatter plot |
| mplot3_lolli | 'mplot3' Lollipop Plot |
| mplot3_missing | Plot missingness |
| mplot3_mosaic | Mosaic plot |
| mplot3_pr | 'mplot3' Precision Recall curves |
| mplot3_prp | Plot CART Decision Tree |
| mplot3_res | 'mplot3' Plot 'resample' |
| mplot3_roc | 'mplot3' ROC curves |
| mplot3_surv | 'mplot3': Survival Plots |
| mplot3_survfit | 'mplot3': Plot 'survfit' objects |
| mplot3_varimp | 'mplot3': Variable Importance |
| mplot3_x | 'mplot3': Univariate plots: index, histogram, density,... |
| mplot3_xy | 'mplot3': XY Scatter and line plots |
| mplot3_xym | Scatter plot with marginal density and/or histogram |
| mplot_AGGTEobj | Plot AGGTEobj object |
| mplot_hsv | Plot HSV color range |
| mplot_raster | Plot Array as Raster Image |
| multigplot | Multipanel *ggplot2* plots |
| nCr | n Choose r |
| nlareg | 'rtemis' internal: NonLinear Activation regression (NLAreg) |
| nunique_perfeat | Number of unique values per feature |
| oddsratio | Calculate odds ratio for a 2x2 contingency table |
| oddsratiotable | Odds ratio table from logistic regression |
| oneHot | One hot encoding |
| onehot2factor | Convert one-hot encoded matrix to factor |
| palettize | Palettize colors |
| permute | Create permutations |
| pfread | fread delimited file in parts |
| plotly.heat | Heatmap with 'plotly' |
| plot.massGAM | Plot 'massGAM' object |
| plot.massGLM | Plot 'massGLM' object |
| plot.resample | 'plot' method for 'resample' object |
| plot.rtModCVCalibration | Plot 'rtModCVCalibration' object |
| plot.rtTest | Plot 'rtTest' object |
| precision | Precision (aka PPV) |
| predict.addtree | Predict Method for MediBoost Model |
| predict.boost | Predict method for 'boost' object |
| predict.cartLite | Predict method for 'cartLite' object |
| predict.cartLiteBoostTV | Predict method for 'cartLiteBoostTV' object |
| predict.glmLite | Predict method for 'glmLite' object |
| predict.glmLiteBoostTV | Predict method for 'glmLiteBoostTV' object |
| predict.hytboost | Predict method for 'hytboost' object |
| predict.hytboostnow | Predict method for 'hytboostnow' object |
| predict.hytreenow | Predict method for 'hytreeLite' object |
| predict.hytreew | Predict method for 'hytreew' object |
| predict.LightRuleFit | 'predict' method for 'LightRuleFit' object |
| predict.lihad | Predict method for 'lihad' object |
| predict.linadleaves | Predict method for 'linadleaves' object |
| predict.nlareg | Predict method for 'nlareg' object |
| predict.nullmod | 'rtemis' internal: predict for an object of class 'nullmod' |
| predict.rtBSplines | Predict S3 method for 'rtBSplines' |
| predict.rtModCVCalibration | Predict using calibrated model |
| predict.rtTLS | 'predict.rtTLS': 'predict' method for 'rtTLS' object |
| predict.rulefit | 'predict' method for 'rulefit' object |
| preorderlgb | Preorder Traversal of LightGBM Tree |
| preprocess | Data preprocessing |
| preprocess_ | Data preprocessing (in-place) |
| present | Present elevate models |
| present_gridsearch | Present gridsearch results |
| previewcolor | Preview color v2.0 |
| print.addtree | Print method for 'addtree' object created using s_AddTree |
| print.boost | Print method for boost object |
| print.cartLiteBoostTV | Print method for cartLiteBoostTV object |
| print.CheckData | Print 'CheckData' object |
| print.class_error | Print class_error |
| print.glmLiteBoostTV | Print method for 'glmLiteBoostTV' object |
| print.gridSearch | 'print' method for 'gridSearch' object |
| print.hytboost | Print method for 'hytboost' object |
| print.hytboostnow | Print method for 'boost' object |
| print.lihad | Print method for 'lihad' object |
| print.linadleaves | Print method for 'linadleaves' object |
| print.massGAM | 'print'massGAM object |
| print.massGLM | 'print'massGLM object |
| print.regError | Print 'regError' object |
| print.resample | 'print' method for resample object |
| print.rtBiasVariance | Print method for bias_variance |
| print.rtTLS | 'print.rtTLS': 'print' method for 'rtTLS' object |
| print.surv_error | Print surv_error |
| prune.addtree | Prune AddTree tree |
| psd | Population Standard Deviation |
| qstat | SGE qstat |
| read | Read tabular data from a variety of formats |
| read_config | Read rtemis configuration file |
| recycle | Recycle values of vector to match length of target |
| reg_error | Regression Error Metrics |
| relu | ReLU - Rectified Linear Unit |
| resample | Resampling methods |
| reverseLevels | Reverse factor levels |
| revfactorlevels | Reverse factor level order |
| rfVarSelect | Variable Selection by Random Forest |
| rnormmat | Random Normal Matrix |
| rowMax | Collapse data.frame to vector by getting row max |
| rsd | Coefficient of Variation (Relative standard deviation) |
| rsq | R-squared |
| rstudio_theme_rtemis | Apply rtemis theme for RStudio |
| rtClust-methods | rtClust S3 methods |
| rtDecom-methods | 'print.rtDecom': 'print' method for 'rtDecom' object |
| rtemis_init | Initialize parallel processing and progress reporting |
| rtemis-package | 'rtemis': Machine Learning and Visualization |
| rtemis_palette | Access rtemis palette colors |
| rtInitProjectDir | Initialize Project Directory |
| rtlayout | Create multipanel plots with the 'mplot3' family |
| rtMeta-methods | rtMeta S3 methods |
| rtModBag-methods | rtModBag S3 methods |
| rtModClass-class | 'rtemis' Classification Model Class |
| rtModCV-methods | S3 methods for 'rtModCV' class that differ from those of the... |
| rtModLite-methods | rtModLite S3 methods |
| rtModLog-class | 'rtemis' Supervised Model Log Class |
| rtModLogger-class | 'rtemis' model logger |
| rtMod-methods | 'rtMod' S3 methods |
| rtpalette | 'rtemis' Color Palettes |
| rtPalettes | UCSF Colors |
| rt_reactable | View table using reactable |
| rtROC | Build an ROC curve |
| rt_save | Write 'rtemis' model to RDS file |
| rtset | 'rtemis' default-setting functions |
| rtversion | Get rtemis and OS version info |
| rtXDecom-class | R6 class for 'rtemis' cross-decompositions |
| ruleDist | Rule distance |
| rules2medmod | Convert rules from cutoffs to median/mode and range |
| runifmat | Random Uniform Matrix |
| s_AdaBoost | Adaboost Binary Classifier C |
| s_AddTree | Additive Tree: Tree-Structured Boosting C |
| savePMML | Save rtemis model to PMML file |
| s_BART | Bayesian Additive Regression Trees (C, R) |
| s_BayesGLM | Bayesian GLM |
| s_BRUTO | Projection Pursuit Regression (BRUTO) [R] |
| s_C50 | C5.0 Decision Trees and Rule-Based Models C |
| s_CART | Classification and Regression Trees [C, R, S] |
| s_CTree | Conditional Inference Trees [C, R, S] |
| se | Extract standard error of fit from rtemis model |
| select_clust | Select 'rtemis' Clusterer |
| select_decom | Select 'rtemis' Decomposer |
| selectiter | Select N of learning iterations based on loss |
| select_learn | Select 'rtemis' Learner |
| sensitivity | Sensitivity |
| seql | Sequence generation with automatic cycling |
| setdiffsym | Symmetric Set Difference |
| setup.bag.resample | Set resample parameters for 'rtMod' bagging |
| setup.color | Set colorGrad parameters |
| setup.cv.resample | 'setup.cv.resample': resample defaults for cross-validation |
| setup.decompose | Set decomposition parameters for train_cv '.decompose'... |
| setup.earlystop | Set earlystop parameters |
| setup.GBM | Set s_GBM parameters |
| setup.grid.resample | Set resample parameters for 'gridSearchLearn' |
| setup.LightRuleFit | Set s_LightRuleFit parameters |
| setup.LIHAD | Set s_LIHAD parameters |
| setup.lincoef | Set lincoef parameters |
| setup.MARS | Set s_MARS parameters |
| setup.meta.resample | Set resample parameters for meta model training |
| setup.preprocess | Set preprocess parameters for train_cv '.preprocess' argument |
| setup.Ranger | Set s_Ranger parameters |
| setup.resample | Set resample settings |
| s_EVTree | Evolutionary Learning of Globally Optimal Trees (C, R) |
| s_GAM | Generalized Additive Model (GAM) (C, R) |
| s_GBM | Gradient Boosting Machine [C, R, S] |
| sge_submit | Submit expression to SGE grid |
| s_GLM | Generalized Linear Model (C, R) |
| s_GLMNET | GLM with Elastic Net Regularization [C, R, S] |
| s_GLMTree | Generalized Linear Model Tree [R] |
| s_GLS | Generalized Least Squares [R] |
| s_H2ODL | Deep Learning on H2O (C, R) |
| s_H2OGBM | Gradient Boosting Machine on H2O (C, R) |
| s_H2ORF | Random Forest on H2O (C, R) |
| s_HAL | Highly Adaptive LASSO [C, R, S] |
| sigmoid | Sigmoid function |
| size | Size of matrix or vector |
| s_KNN | k-Nearest Neighbors Classification and Regression (C, R) |
| s_LDA | Linear Discriminant Analysis |
| s_LightCART | LightCART Classification and Regression (C, R) |
| s_LightGBM | LightGBM Classification and Regression (C, R) |
| s_LightRF | Random Forest using LightGBM |
| s_LightRuleFit | RuleFit with LightGBM (C, R) |
| s_LIHAD | The Linear Hard Hybrid Tree: Hard Additive Tree (no gamma)... |
| s_LIHADBoost | Boosting of Linear Hard Additive Trees [R] |
| s_LINAD | Linear Additive Tree (C, R) |
| s_LINOA | Linear Optimized Additive Tree (C, R) |
| s_LM | Linear model |
| s_LMTree | Linear Model Tree [R] |
| s_LOESS | Local Polynomial Regression (LOESS) [R] |
| s_LOGISTIC | Logistic Regression |
| s_MARS | Multivariate adaptive regression splines (MARS) (C, R) |
| s_MLRF | Spark MLlib Random Forest (C, R) |
| s_MULTINOM | Multinomial Logistic Regression |
| s_NBayes | Naive Bayes Classifier C |
| s_NLA | NonLinear Activation unit Regression (NLA) [R] |
| s_NLS | Nonlinear Least Squares (NLS) [R] |
| s_NW | Nadaraya-Watson kernel regression [R] |
| softmax | Softmax function |
| softplus | Softplus function |
| sortedlines | lines, but sorted |
| sparsernorm | Sparse rnorm |
| sparseVectorSummary | Sparseness and pairwise correlation of vectors |
| sparsify | Sparsify a vector |
| specificity | Specificity |
| s_POLY | Polynomial Regression |
| s_PolyMARS | Multivariate adaptive polynomial spline regression (POLYMARS)... |
| s_PPR | Projection Pursuit Regression (PPR) [R] |
| s_PSurv | Parametric Survival Regression [S] |
| s_QDA | Quadratic Discriminant Analysis C |
| s_QRNN | Quantile Regression Neural Network [R] |
| s_Ranger | Random Forest Classification and Regression (C, R) |
| s_RF | Random Forest Classification and Regression (C, R) |
| s_RFSRC | Random Forest for Classification, Regression, and Survival... |
| s_RLM | Robust linear model |
| s_RuleFit | Rulefit [C, R] |
| s_SDA | Sparse Linear Discriminant Analysis |
| s_SGD | Stochastic Gradient Descent (SGD) (C, R) |
| s_SPLS | Sparse Partial Least Squares Regression (C, R) |
| s_SVM | Support Vector Machines (C, R) |
| stderror | Standard Error of the Mean |
| s_TFN | Feedforward Neural Network with 'tensorflow' (C, R) |
| s_TLS | Total Least Squares Regression [R] |
| strata2factor | Convert 'survfit' object's strata to a factor |
| strat.boot | Stratified Bootstrap Resampling |
| strat.sub | Resample using Stratified Subsamples |
| strict | Strict assignment by class or type |
| strng | String formatting utilities |
| summarize | Summarize numeric variables |
| summary.massGAM | 'massGAM' object summary |
| summary.massGLM | 'massGLM' object summary |
| surv_error | Survival Analysis Metrics |
| svd1 | 'rtemis-internals' Project Variables to First Eigenvector |
| s_XGBoost | XGBoost Classification and Regression (C, R) |
| s_XRF | XGBoost Random Forest Classification and Regression (C, R) |
| synth_multimodal | Create "Multimodal" Synthetic Data |
| synth_reg_data | Synthesize Simple Regression Data |
| table1 | Table 1 |
| theme | Themes for 'mplot3' and 'dplot3' functions |
| themes | Print available rtemis themes |
| timeProc | Time a process |
| tohtml | Generate 'CheckData' object description in HTML |
| train_cv | Tune, Train, and Test an 'rtemis' Learner by Nested... |
| tunable | Print tunable hyperparameters for a supervised learning... |
| typeset | Set type of columns |
| uci_heart_failure | UCI Heart Failure Data |
| uniprot_get | Get protein sequence from UniProt |
| uniquevalsperfeat | Unique values per feature |
| winsorize | Winsorize vector |
| x_CCA | Sparse Canonical Correlation Analysis (CCA) |
| xlsx2list | Read all sheets of an XLSX file into a list |
| xselect_decom | Select 'rtemis' cross-decomposer |
| xtdescribe | Describe longitudinal dataset |
| zip2longlat | Get Longitude and Lattitude for zip code(s) |
| zipdist | Get distance between pairs of zip codes |
