| '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 using GAM | 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 |
| 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 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 |
| 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 |
| UCSF Colors | amazonCol appleCol berkeleyCol brownCol californiaCol calpolyCol caltechCol chicagoCol cmuCol columbiaCol cornellCol csuCol dartmouthCol emoryCol ethCol firefoxCol googleCol hawaiiCol hmsCol illinoisCol imperialCol iowaCol jeffersonCol jhuCol mcgillCol michiganCol microsoftCol mitCol mozillaCol msuCol nhsCol nihCol nyuCol oxfordCol pennCol pennLightPalette pennPalette pennstateCol princetonCol rtPalettes rwthCol scrippsCol sfsuCol stanfordCol techCol texasCol torontoCol ubcCol ucdCol uciCol uclaCol uclCol ucmercedCol ucrColor ucsbCol ucscCol ucsdCol ucsfLegacyCol ucsfPalette umdCol usfCol uwCol vanderbiltCol washuCol yaleCol |
| 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 |
| 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 |
