'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 |