| 'rtemis': Machine Learning and Visualization | rtemis-package rtemis |
| Binary matrix times character vector | %BC% |
| Check for constant columns | any_constant |
| Convert 'linadleaves' to 'data.tree' object | as.data.tree.linadleaves |
| Convert 'rpart' rules to 'data.tree' object | as.data.tree.rpart |
| Convert 'shyoptleaves' to 'data.tree' object | as.data.tree.shyoptleaves |
| Area under the ROC Curve | auc |
| Area under the Curve by pairwise concordance | auc_pairs |
| Balanced Accuracy | bacc |
| Extract coefficients from Additive Tree leaves | betas.lihad |
| Bias-Variance Decomposition | bias_variance |
| Binary matrix times character vector | binmat2vec |
| String formatting utilities | bold cyan gray green hilite hilitebig italic magenta orange red reset underline |
| Boost an 'rtemis' learner for regression | boost |
| Bootstrap Resampling | bootstrap |
| Brier Score | brier_score |
| Fuzzy C-means Clustering | c_CMeans |
| Density-based spatial clustering of applications with noise | c_DBSCAN |
| Expectation Maximization Clustering | c_EMC |
| K-Means Clustering with H2O | c_H2OKMeans |
| Clustering by Hard Competitive Learning | c_HARDCL |
| Hierarchical Ordered Partitioning and Collapsing Hybrid | c_HOPACH |
| K-means Clustering | c_KMeans |
| Mean Shift Clustering | c_MeanShift |
| Neural Gas Clustering | c_NGAS |
| Partitioning Around Medoids | c_PAM |
| Partitioning Around Medoids with k Estimation | c_PAMK |
| Spectral Clustering | c_SPEC |
| Calibrate predicted probabilities | calibrate |
| Calibrate cross-validated model | calibrate_cv |
| Print range of continuous variable | catrange |
| Print Size | catsize |
| Check Data | check_data |
| Check file(s) exist | check_files |
| Early stopping check | checkpoint_earlystop |
| Chill | chill |
| Classification Error | class_error |
| Class Imbalance | class_imbalance |
| Clean column names | clean_colnames |
| Clean names | clean_names |
| Clustering with 'rtemis' | clust |
| Extract coefficients from Hybrid Additive Tree leaves | coef.lihad |
| Color to Grayscale | col2grayscale |
| Convert R color to hexadecimal code | col2hex |
| Collapse data.frame to vector by getting column max | colMax |
| Fade color towards target | color_fade |
| Invert Color in RGB space | color_invertRGB |
| Average colors | color_mean |
| Order colors | color_order |
| Separate colors | color_separate |
| Squared Color Distance | color_sqdist |
| Adjust HSV Color | colorAdjust |
| Color Gradient | colorGrad |
| Color gradient for continuous variable | colorGrad.x |
| Color gradient for continuous variable | colorgradient.x |
| Create an alternating sequence of graded colors | colorMix |
| Simple Color Operations | colorOp |
| Convert data frame columns to list elements | cols2list |
| Create rtemis configuration file | create_config |
| Combine rules | crules |
| Autoencoder using H2O | d_H2OAE |
| Generalized Low-Rank Models (GLRM) on H2O | d_H2OGLRM |
| Independent Component Analysis | d_ICA |
| Isomap | d_Isomap |
| Kernel Principal Component Analysis | d_KPCA |
| Locally Linear Embedding | d_LLE |
| Multidimensional Scaling | d_MDS |
| Non-negative Matrix Factorization (NMF) | d_NMF |
| Principal Component Analysis | d_PCA |
| Sparse Principal Component Analysis | d_SPCA |
| Singular Value Decomposition | d_SVD |
| t-distributed Stochastic Neighbor Embedding | d_TSNE |
| Uniform Manifold Approximation and Projection (UMAP) | d_UMAP |
| B-Spline matrix from dataset | dat2bsplinemat |
| Create n-degree polynomial from data frame | dat2poly |
| Date to factor time bin | date2factor |
| Date to year-month factor | date2ym |
| Date to year-quarter factor | date2yq |
| Extract features from dates | dates2features |
| Collect a lazy-read duckdb table | ddb_collect |
| Read CSV using DuckDB | ddb_data |
| Format Numbers for Printing | ddSci |
| Matrix Decomposition with 'rtemis' | decom |
| 'rtemis' internal: Dependencies check | dependency_check |
| Pastelify a color (make a color more pastel) | desaturate |
| Describe generic | describe |
| Move data frame column | df_movecolumn |
| Distill rules from trained RF and GBM learners | distillTreeRules |
| Plot AddTree trees | dplot3_addtree |
| Interactive Barplots | dplot3_bar |
| Interactive Boxplots & Violin plots | dplot3_box |
| Draw calibration plot | dplot3_calibration |
| Plot 'rpart' decision trees | dplot3_cart |
| Plot confusion matrix | dplot3_conf |
| True vs. Predicted Plot | dplot3_fit |
| Plot graph using 'networkD3' | dplot3_graphd3 |
| Plot network using 'threejs::graphjs' | dplot3_graphjs |
| Interactive Heatmaps | dplot3_heatmap |
| Plot interactive choropleth map using 'leaflet' | dplot3_leaflet |
| Plot a Linear Additive Tree trained by s_LINAD using _visNetwork_ | dplot3_linad |
| Interactive Pie Chart | dplot3_pie |
| Plot the amino acid sequence with annotations | dplot3_protein |
| Barplot p-values using dplot3_bar | dplot3_pvals |
| Interactive Spectrogram | dplot3_spectrogram |
| Simple HTML table | dplot3_table |
| Interactive Timeseries Plots | dplot3_ts |
| Interactive Variable Importance Plot | dplot3_varimp |
| Volcano Plot | dplot3_volcano |
| Interactive Univariate Plots | dplot3_x |
| Plot timeseries data | dplot3_xt |
| Interactive Scatter Plots | dplot3_xy |
| Interactive 3D Plots | dplot3_xyz |
| Set Dynamic Range | drange |
| Check if all levels in a column are unique | dt_check_unique |
| Describe data.table | dt_describe |
| Tabulate column attributes | dt_get_column_attr |
| Get index of duplicate values | dt_get_duplicates |
| Get factor levels from data.table | dt_get_factor_levels |
| Index columns by attribute name & value | dt_index_attr |
| Inspect column types | dt_inspect_type |
| Long to wide key-value reshaping | dt_keybin_reshape |
| Merge data.tables | dt_merge |
| List column names by attribute | dt_names_by_attr |
| List column names by class | dt_names_by_class |
| Get N and percent match of values between two columns of two data.tables | dt_pctmatch |
| Get percent of missing values from every column | dt_pctmissing |
| Set column types automatically | dt_set_autotypes |
| Clean column names and factor levels in-place | dt_set_clean_all |
| Clean factor levels of data.table in-place | dt_set_cleanfactorlevels |
| Convert data.table logical columns to factor with custom labels in-place | dt_set_logical2factor |
| Early stopping | earlystop |
| Expand boosting series | expand.boost |
| Explain individual-level model predictions | explain |
| F1 score | f1 |
| Factor harmonize | factor_harmonize |
| Factor NA to "missing" level | factor_NA2missing |
| Factor Analysis | factoryze |
| Decribe factor | fct_describe |
| Format method for 'call' objects | format.call |
| Format LightRuleFit rules | formatLightRules |
| Format rules | formatRules |
| FWHM to Sigma | fwhm2sigma |
| Get holidays from date vector | get_holidays |
| Get version of all loaded packages (namespaces) | get_loaded_pkg_version |
| Get the mode of a factor or integer | get_mode |
| Get RuleFit rules | get_rules |
| Extract variable names from rules | get_vars_from_rules |
| Get factor/numeric/logical/character names from data.frame/data.table | get-names getfactornames |
| Get names by string matching | getcharacternames getdatenames getlogicalnames getnames getnumericnames |
| Get data.frame names and types | getnamesandtypes |
| 'rtemis' 'ggplot2' dark theme | ggtheme_dark |
| 'rtemis' 'ggplot2' light theme | ggtheme_light |
| Bare bones decision tree derived from 'rpart' | glmLite |
| Geometric mean | gmean |
| Bayesian Gaussian Processes [R] | gp |
| Node-wise (i.e. vertex-wise) graph metrics | graph_node_metrics |
| 'rtemis' internal: Grid check | gridCheck |
| Greater-than Table | gtTable |
| Basic Bivariate Hypothesis Testing and Plotting | htest |
| Inspect character and factor vector | inspect_type |
| Inverse Logit | invlogit |
| Check if vector is constant | is_constant |
| Check if variable is discrete (factor or integer) | is_discrete |
| K-fold Resampling | kfold |
| Format text for label printing | labelify |
| Linear Model Coefficients | lincoef |
| Write list elements to CSV files | list2csv |
| Logistic function | logistic |
| Logit transform | logit |
| Log Loss for a binary classifier | logloss |
| Leave-one-out Resampling | loocv |
| Connectivity Matrix to Edge List | lotri2edgeList |
| 'lsapply' | lsapply |
| Make key from data.table id - description columns | make_key |
| Mass-univariate GAM Analysis | massGAM |
| Mass-univariate GLM Analysis | massGLAM |
| Mass-univariate GLM Analysis | massGLM |
| Mass-univariate Analysis | massUni |
| Match cases by covariates | matchcases |
| Merge panel data treatment and outcome data | mergelongtreatment |
| Meta Models for Regression (Model Stacking) | meta_mod |
| Get names by string matching multiple patterns | mgetnames |
| Histograms | mhist |
| Add legend to 'mplot3' plot | mlegend |
| Error Metrics for Supervised Learning | mod_error |
| Plot AGGTEobj object | mplot_AGGTEobj |
| Plot HSV color range | mplot_hsv |
| Plot Array as Raster Image | mplot_raster |
| 'mplot3': ADSR Plot | mplot3_adsr |
| 'mplot3': Barplot | mplot3_bar |
| 'mplot3': Boxplot | mplot3_box |
| Plot confusion matrix | mplot3_conf |
| Plot extended confusion matrix for binary classification | mplot3_confbin |
| 'mplot3': Decision boundaries | mplot3_decision |
| True vs. Fitted plot | mplot3_fit |
| 'mplot3': Guitar Fretboard | mplot3_fret |
| Plot 'igraph' networks | mplot3_graph |
| Plot a harmonograph | mplot3_harmonograph |
| 'mplot3' Heatmap ('image'; modified 'heatmap') | mplot3_heatmap |
| Draw image (False color 2D) | mplot3_img |
| Laterality scatter plot | mplot3_laterality |
| 'mplot3' Lollipop Plot | mplot3_lolli |
| Plot missingness | mplot3_missing |
| Mosaic plot | mplot3_mosaic |
| 'mplot3' Precision Recall curves | mplot3_pr |
| Plot CART Decision Tree | mplot3_prp |
| 'mplot3' Plot 'resample' | mplot3_res |
| 'mplot3' ROC curves | mplot3_roc |
| 'mplot3': Survival Plots | mplot3_surv |
| 'mplot3': Plot 'survfit' objects | mplot3_survfit |
| 'mplot3': Variable Importance | mplot3_varimp |
| 'mplot3': Univariate plots: index, histogram, density, QQ-line | mplot3_x |
| 'mplot3': XY Scatter and line plots | mplot3_xy |
| Scatter plot with marginal density and/or histogram | mplot3_xym |
| Error functions | mae mse msew rmse |
| Multipanel *ggplot2* plots | multigplot |
| n Choose r | nCr |
| Number of unique values per feature | nunique_perfeat |
| Calculate odds ratio for a 2x2 contingency table | oddsratio |
| Odds ratio table from logistic regression | oddsratiotable |
| One hot encoding | dt_set_oneHot oneHot oneHot.data.frame oneHot.data.table oneHot.default |
| Convert one-hot encoded matrix to factor | onehot2factor |
| Palettize colors | palettize |
| Create permutations | permute |
| fread delimited file in parts | pfread |
| Plot 'massGAM' object | plot.massGAM |
| Plot 'massGLM' object | plot.massGLM |
| 'plot' method for 'resample' object | plot.resample |
| Plot 'rtModCVCalibration' object | plot.rtModCVCalibration |
| Plot 'rtTest' object | plot.rtTest |
| Heatmap with 'plotly' | plotly.heat |
| Precision (aka PPV) | precision |
| Predict Method for MediBoost Model | predict.addtree |
| Predict method for 'boost' object | predict.boost |
| Predict method for 'cartLite' object | predict.cartLite |
| Predict method for 'cartLiteBoostTV' object | predict.cartLiteBoostTV |
| Predict method for 'glmLite' object | predict.glmLite |
| Predict method for 'glmLiteBoostTV' object | predict.glmLiteBoostTV |
| Predict method for 'hytboost' object | predict.hytboost |
| Predict method for 'hytboostnow' object | predict.hytboostnow |
| Predict method for 'hytreeLite' object | predict.hytreenow |
| Predict method for 'hytreew' object | predict.hytreew |
| 'predict' method for 'LightRuleFit' object | predict.LightRuleFit |
| Predict method for 'lihad' object | predict.lihad |
| Predict method for 'linadleaves' object | predict.linadleaves |
| Predict method for 'nlareg' object | predict.nlareg |
| 'rtemis' internal: predict for an object of class 'nullmod' | predict.nullmod |
| Predict S3 method for 'rtBSplines' | predict.rtBSplines |
| Predict using calibrated model | predict.rtModCVCalibration |
| 'predict.rtTLS': 'predict' method for 'rtTLS' object | predict.rtTLS |
| 'predict' method for 'rulefit' object | predict.rulefit |
| Data preprocessing | preprocess |
| Data preprocessing (in-place) | preprocess_ |
| Present elevate models | present |
| Present gridsearch results | present_gridsearch |
| Preview color v2.0 | previewcolor |
| Print method for 'addtree' object created using s_AddTree | print.addtree |
| Print method for boost object | print.boost |
| Print method for cartLiteBoostTV object | print.cartLiteBoostTV |
| Print 'CheckData' object | print.CheckData |
| Print class_error | print.class_error |
| Print method for 'glmLiteBoostTV' object | print.glmLiteBoostTV |
| 'print' method for 'gridSearch' object | print.gridSearch |
| Print method for 'hytboost' object | print.hytboost |
| Print method for 'boost' object | print.hytboostnow |
| Print method for 'lihad' object | print.lihad |
| Print method for 'linadleaves' object | print.linadleaves |
| 'print'massGAM object | print.massGAM |
| 'print'massGLM object | print.massGLM |
| Print 'regError' object | print.regError |
| 'print' method for resample object | print.resample |
| Print method for bias_variance | print.rtBiasVariance |
| 'print.rtDecom': 'print' method for 'rtDecom' object | print.rtDecom |
| 'print.rtTLS': 'print' method for 'rtTLS' object | print.rtTLS |
| Print surv_error | print.surv_error |
| Convert probabilities to categorical (factor) | prob2categorical |
| Prune AddTree tree | prune.addtree |
| Population Standard Deviation | psd |
| SGE qstat | qstat |
| Read tabular data from a variety of formats | read |
| Read rtemis configuration file | read_config |
| Recycle values of vector to match length of target | recycle |
| Regression Error Metrics | reg_error |
| ReLU - Rectified Linear Unit | relu |
| Resampling methods | resample |
| Reverse factor levels | reverseLevels |
| Reverse factor level order | revfactorlevels |
| Variable Selection by Random Forest | rfVarSelect |
| Random Normal Matrix | rnormmat |
| Collapse data.frame to vector by getting row max | rowMax |
| Coefficient of Variation (Relative standard deviation) | rsd |
| R-squared | rsq |
| Apply rtemis theme for RStudio | rstudio_theme_rtemis |
| View table using reactable | rt_reactable |
| Write 'rtemis' model to RDS file | rt_save |
| rtClust S3 methods | print.rtClust rtClust-methods |
| Access rtemis palette colors | rtemis_palette |
| Initialize Project Directory | rtInitProjectDir |
| Create multipanel plots with the 'mplot3' family | rtlayout |
| rtMeta S3 methods | predict.rtMeta rtMeta-methods |
| 'rtMod' S3 methods | coef.rtMod fitted.rtMod plot.rtMod predict.rtMod predict.rtModLite print.rtMod residuals.rtMod rtMod-methods summary.rtMod |
| rtModBag S3 methods | predict.rtModBag rtModBag-methods |
| 'rtemis' Classification Model Class | rtModClass rtModClass-class |
| S3 methods for 'rtModCV' class that differ from those of the 'rtMod' superclass | describe.rtModCV plot.rtModCV predict.rtModCV rtModCV-methods summary.rtModCV |
| rtModLite S3 methods | print.rtModLite rtModLite-methods |
| 'rtemis' Supervised Model Log Class | rtModLog rtModLog-class |
| 'rtemis' model logger | rtModLogger rtModLogger-class |
| 'rtemis' Color Palettes | rtpalette |
| Build an ROC curve | rtROC |
| 'rtemis' default-setting functions | rtset |
| Get rtemis and OS version info | rtversion |
| R6 class for 'rtemis' cross-decompositions | rtXDecom rtXDecom-class |
| Rule distance | ruleDist |
| Convert rules from cutoffs to median/mode and range | rules2medmod |
| Random Uniform Matrix | runifmat |
| Adaboost Binary Classifier C | s_AdaBoost |
| Additive Tree: Tree-Structured Boosting C | s_AddTree |
| Bayesian Additive Regression Trees (C, R) | s_BART |
| Bayesian GLM | s_BayesGLM |
| Projection Pursuit Regression (BRUTO) [R] | s_BRUTO |
| C5.0 Decision Trees and Rule-Based Models C | s_C50 |
| Classification and Regression Trees [C, R, S] | s_CART |
| Conditional Inference Trees [C, R, S] | s_CTree |
| Evolutionary Learning of Globally Optimal Trees (C, R) | s_EVTree |
| Generalized Additive Model (GAM) (C, R) | s_GAM |
| Gradient Boosting Machine [C, R, S] | s_GBM |
| Generalized Linear Model (C, R) | s_GLM |
| GLM with Elastic Net Regularization [C, R, S] | s_GLMNET |
| Generalized Linear Model Tree [R] | s_GLMTree |
| Generalized Least Squares [R] | s_GLS |
| Deep Learning on H2O (C, R) | s_H2ODL |
| Gradient Boosting Machine on H2O (C, R) | s_H2OGBM |
| Random Forest on H2O (C, R) | s_H2ORF |
| Highly Adaptive LASSO [C, R, S] | s_HAL |
| Classification and Regression Trees [C, R, S] | predict.Isotonic s_Isotonic |
| k-Nearest Neighbors Classification and Regression (C, R) | s_KNN |
| Linear Discriminant Analysis | s_LDA |
| LightCART Classification and Regression (C, R) | s_LightCART |
| LightGBM Classification and Regression (C, R) | s_LightGBM |
| Random Forest using LightGBM | s_LightRF |
| RuleFit with LightGBM (C, R) | s_LightRuleFit |
| The Linear Hard Hybrid Tree: Hard Additive Tree (no gamma) with Linear Nodes [R] | s_LIHAD |
| Boosting of Linear Hard Additive Trees [R] | s_LIHADBoost |
| Linear Additive Tree (C, R) | s_LINAD |
| Linear Optimized Additive Tree (C, R) | s_LINOA |
| Linear model | s_LM |
| Linear Model Tree [R] | s_LMTree |
| Local Polynomial Regression (LOESS) [R] | s_LOESS |
| Logistic Regression | s_Logistic |
| Multivariate adaptive regression splines (MARS) (C, R) | s_MARS |
| Spark MLlib Random Forest (C, R) | s_MLRF |
| Multinomial Logistic Regression | s_MULTINOM |
| Naive Bayes Classifier C | s_NBayes |
| NonLinear Activation unit Regression (NLA) [R] | s_NLA |
| Nonlinear Least Squares (NLS) [R] | s_NLS |
| Nadaraya-Watson kernel regression [R] | s_NW |
| Polynomial Regression | s_POLY |
| Multivariate adaptive polynomial spline regression (POLYMARS) (C, R) | s_PolyMARS |
| Projection Pursuit Regression (PPR) [R] | s_PPR |
| Parametric Survival Regression [S] | s_PSurv |
| Quadratic Discriminant Analysis C | s_QDA |
| Quantile Regression Neural Network [R] | s_QRNN |
| Random Forest Classification and Regression (C, R) | s_Ranger |
| Random Forest Classification and Regression (C, R) | s_RF |
| Random Forest for Classification, Regression, and Survival [C, R, S] | s_RFSRC |
| Robust linear model | s_RLM |
| Rulefit [C, R] | s_RuleFit |
| Sparse Linear Discriminant Analysis | s_SDA |
| Stochastic Gradient Descent (SGD) (C, R) | s_SGD |
| Sparse Partial Least Squares Regression (C, R) | s_SPLS |
| Support Vector Machines (C, R) | s_SVM |
| Feedforward Neural Network with 'tensorflow' (C, R) | s_TFN |
| Total Least Squares Regression [R] | s_TLS |
| XGBoost Classification and Regression (C, R) | s_XGBoost |
| XGBoost Random Forest Classification and Regression (C, R) | s_XRF |
| Save rtemis model to PMML file | savePMML |
| Extract standard error of fit from rtemis model | se |
| Select 'rtemis' Clusterer | select_clust |
| Select 'rtemis' Decomposer | select_decom |
| Select 'rtemis' Learner | select_learn |
| Select N of learning iterations based on loss | selectiter |
| Sensitivity | sensitivity |
| Sequence generation with automatic cycling | seql |
| Symmetric Set Difference | setdiffsym |
| Set resample parameters for 'rtMod' bagging | setup.bag.resample |
| Set colorGrad parameters | setup.color |
| 'setup.cv.resample': resample defaults for cross-validation | setup.cv.resample |
| Set decomposition parameters for train_cv '.decompose' argument | setup.decompose |
| Set earlystop parameters | setup.earlystop |
| Set s_GBM parameters | setup.GBM |
| Set resample parameters for 'gridSearchLearn' | setup.grid.resample |
| Set s_LightRuleFit parameters | setup.LightRuleFit |
| Set s_LIHAD parameters | setup.LIHAD |
| Set lincoef parameters | setup.lincoef |
| Set s_MARS parameters | setup.MARS |
| Set resample parameters for meta model training | setup.meta.resample |
| Set preprocess parameters for train_cv '.preprocess' argument | setup.preprocess |
| Set s_Ranger parameters | setup.Ranger |
| Set resample settings | setup.resample |
| Submit expression to SGE grid | sge_submit |
| Sigmoid function | sigmoid |
| Size of matrix or vector | size |
| Softmax function | softmax |
| Softplus function | softplus |
| lines, but sorted | sortedlines |
| Sparse rnorm | sparsernorm |
| Sparseness and pairwise correlation of vectors | sparseVectorSummary |
| Sparsify a vector | sparsify |
| Specificity | specificity |
| Standard Error of the Mean | stderror |
| Stratified Bootstrap Resampling | strat.boot |
| Resample using Stratified Subsamples | strat.sub |
| Convert 'survfit' object's strata to a factor | strata2factor |
| Summarize numeric variables | summarize |
| 'massGAM' object summary | summary.massGAM |
| 'massGLM' object summary | summary.massGLM |
| Survival Analysis Metrics | surv_error |
| 'rtemis-internals' Project Variables to First Eigenvector | svd1 |
| Create "Multimodal" Synthetic Data | synth_multimodal |
| Synthesize Simple Regression Data | synth_reg_data |
| Table 1 | table1 |
| Themes for 'mplot3' and 'dplot3' functions | theme_black theme_blackgrid theme_blackigrid theme_darkgray theme_darkgraygrid theme_darkgrayigrid theme_lightgraygrid theme_mediumgraygrid theme_white theme_whitegrid theme_whiteigrid |
| Print available rtemis themes | themes |
| Time a process | timeProc |
| Generate 'CheckData' object description in HTML | tohtml |
| Tune, Train, and Test an 'rtemis' Learner by Nested Resampling | train_cv |
| Print tunable hyperparameters for a supervised learning algorithm | tunable |
| Set type of columns | typeset |
| UCI Heart Failure Data | uci_heart_failure |
| Get protein sequence from UniProt | uniprot_get |
| Unique values per feature | uniquevalsperfeat |
| Winsorize vector | winsorize |
| Sparse Canonical Correlation Analysis (CCA) | x_CCA |
| Read all sheets of an XLSX file into a list | xlsx2list |
| Select 'rtemis' cross-decomposer | xselect_decom |
| Describe longitudinal dataset | xtdescribe |
| Get Longitude and Lattitude for zip code(s) | zip2longlat |
| Get distance between pairs of zip codes | zipdist |
