Man pages for egenn/rtemis
Machine Learning and Visualization

any_constantCheck for constant columns
as.data.tree.linadleavesConvert 'linadleaves' to 'data.tree' object
as.data.tree.rpartConvert 'rpart' rules to 'data.tree' object
as.data.tree.shyoptleavesConvert 'shyoptleaves' to 'data.tree' object
aucArea under the ROC Curve
auc_pairsArea under the Curve by pairwise concordance
baccBalanced Accuracy
bagBag an 'rtemis' learner for regression or classification (C,...
betas.lihadExtract coefficients from Additive Tree leaves
bias_varianceBias-Variance Decomposition
binmat2vecBinary matrix times character vector
boostBoost an 'rtemis' learner for regression
bootstrapBootstrap Resampling
calibrateCalibrate predicted probabilities using GAM
calibrate_cvCalibrate cross-validated model
calibrate_slideCalibrate predicted probabilities using GAM
cartLiteBoostTVBoost an 'rtemis' learner for regression
catrangePrint range of continuous variable
catsizePrint Size
c_CMeansFuzzy C-means Clustering
c_DBSCANDensity-based spatial clustering of applications with noise
c_EMCExpectation Maximization Clustering
c_H2OKMeansK-Means Clustering with H2O
c_HARDCLClustering by Hard Competitive Learning
check_dataCheck Data
check_filesCheck file(s) exist
checkpoint_earlystopEarly stopping check
chillChill
c_HOPACHHierarchical Ordered Partitioning and Collapsing Hybrid
c_KMeansK-means Clustering
class_errorClassification Error
class_imbalanceClass Imbalance
clean_colnamesClean column names
clean_namesClean names
clustClustering with 'rtemis'
c_MeanShiftMean Shift Clustering
c_NGASNeural Gas Clustering
coef.lihadExtract coefficients from Hybrid Additive Tree leaves
col2grayscaleColor to Grayscale
col2hexConvert R color to hexadecimal code
colMaxCollapse data.frame to vector by getting column max
colorAdjustAdjust HSV Color
color_fadeFade color towards target
colorGradColor Gradient
colorgradient.xColor gradient for continuous variable
colorGrad.xColor gradient for continuous variable
color_invertRGBInvert Color in RGB space
color_meanAverage colors
colorMixCreate an alternating sequence of graded colors
colorOpSimple Color Operations
color_orderOrder colors
color_separateSeparate colors
color_sqdistSquared Color Distance
cols2listConvert data frame columns to list elements
c_PAMPartitioning Around Medoids
c_PAMKPartitioning Around Medoids with k Estimation
crulesCombine rules
c_SPECSpectral Clustering
dat2bsplinematB-Spline matrix from dataset
dat2polyCreate n-degree polynomial from data frame
date2factorDate to factor time bin
date2ymDate to year-month factor
date2yqDate to year-quarter factor
ddb_collectCollect a lazy-read duckdb table
ddb_dataRead CSV using DuckDB
ddSciFormat Numbers for Printing
decomMatrix Decomposition with 'rtemis'
dependency_check'rtemis' internal: Dependencies check
desaturatePastelify a color (make a color more pastel)
describeDescribe generic
df_movecolumnMove data frame column
d_H2OAEAutoencoder using H2O
d_H2OGLRMGeneralized Low-Rank Models (GLRM) on H2O
d_ICAIndependent Component Analysis
d_IsomapIsomap
distillTreeRulesDistill rules from trained RF and GBM learners
d_KPCAKernel Principal Component Analysis
d_LLELocally Linear Embedding
d_MDSMultidimensional Scaling
d_NMFNon-negative Matrix Factorization (NMF)
d_PCAPrincipal Component Analysis
dplot3_addtreePlot AddTree trees
dplot3_barInteractive Barplots
dplot3_boxInteractive Boxplots & Violin plots
dplot3_calibrationDraw calibration plot
dplot3_cartPlot 'rpart' decision trees
dplot3_graphd3Plot graph using 'networkD3'
dplot3_graphjsPlot network using 'threejs::graphjs'
dplot3_heatmapInteractive Heatmaps
dplot3_leafletPlot interactive choropleth map using 'leaflet'
dplot3_linadPlot a Linear Additive Tree trained by 's_LINAD' using...
dplot3_pieInteractive Pie Chart
dplot3_proteinPlot the primary amino acid sequence of a protein
dplot3_pvalsBarplot p-values using dplot3_bar
dplot3_shytreecoefCoefficient heatmap for The Hybrid Tree
dplot3_spectrogramInteractive Spectrogram
dplot3_tableSimple HTML table
dplot3_tsInteractive Timeseries Plots
dplot3_varimpInteractive Variable Importance Plot
dplot3_volcanoVolcano Plot
dplot3_xInteractive Univariate Plots
dplot3_xyInteractive Scatter Plots
dplot3_xyzInteractive 3D Plots
drangeSet Dynamic Range
d_SPCASparse Principal Component Analysis
d_SVDSingular Value Decomposition
dt_check_uniqueCheck if all levels in a column are unique
dt_describeDescribe data.table
dt_get_column_attrTabulate column attributes
dt_get_duplicatesGet index of duplicate values
dt_get_factor_levelsGet factor levels from data.table
dt_index_attrIndex columns by attribute name & value
dt_inspect_typeInspect column types
dt_keybin_reshapeLong to wide key-value reshaping
dt_mergeMerge data.tables
dt_names_by_attrList column names by attribute
dt_names_by_classList column names by class
dt_NuniqueperfeatNumber of unique values per feature
dt_pctmatchGet N and percent match of values between two columns of two...
dt_pctmissingGet percent of missing values from every column
dt_set_autotypesSet column types automatically
dt_set_clean_allClean column names and factor levels in-place
dt_set_cleanfactorlevelsClean factor levels of data.table in-place
dt_set_logical2factorConvert data.table logical columns to factor with custom...
d_TSNEt-distributed Stochastic Neighbor Embedding
d_UMAPUniform Manifold Approximation and Projection (UMAP)
earlystopEarly stopping
errorError functions
expand.boostExpand boosting series
explainExplain individual-level model predictions
f1F1 score
factor_harmonizeFactor harmonize
factor_NA2missingFactor NA to "missing" level
factoryzeFactor Analysis
fct_describeDecribe factor
format.callFormat method for 'call' objects
formatLightRulesFormat LightRuleFit rules
formatRulesFormat rules
fwhm2sigmaFWHM to Sigma
get_loaded_pkg_versionGet version of all loaded packages (namespaces)
get_modeGet the mode of a factor or integer
getnamesGet names by string matching
get-namesGet factor/numeric/logical/character names from...
getnamesandtypesGet data.frame names and types
get_rulesGet RuleFit rules
get_vars_from_rulesExtract variable names from rules
ggtheme_dark'rtemis' 'ggplot2' dark theme
ggtheme_light'rtemis' 'ggplot2' light theme
glmLiteBare bones decision tree derived from 'rpart'
gmeanGeometric mean
gpBayesian Gaussian Processes [R]
grapes-BC-grapesBinary matrix times character vector
graph_node_metricsNode-wise (i.e. vertex-wise) graph metrics
gridCheck'rtemis' internal: Grid check
gtTableGreater-than Table
htestBasic Bivariate Hypothesis Testing and Plotting
inspect_typeInspect character and factor vector
invlogitInverse Logit
is_constantCheck if vector is constant
is_discreteCheck if variable is discrete (factor or integer)
kfoldK-fold Resampling
labelifyFormat text for label printing
learnSupervised Learning with 'rtemis'
lincoefLinear Model Coefficients
list2csvWrite list elements to CSV files
logisticLogistic function
logitLogit transform
loglossLog Loss for a binary classifier
loocvLeave-one-out Resampling
lotri2edgeListConnectivity Matrix to Edge List
lsapply'lsapply'
make_keyMake key from data.table id - description columns
massGAMMass-univariate GAM Analysis
massGLAMMass-univariate GLM Analysis
massGLMMass-univariate GLM Analysis
massUniMass-univariate Analysis
matchcasesMatch cases by covariates
mergelongtreatmentMerge panel data treatment and outcome data
meta_modMeta Models for Regression (Model Stacking)
mgetnamesGet names by string matching multiple patterns
mhistHistograms
mlegendAdd legend to 'mplot3' plot
mod_errorError Metrics for Supervised Learning
mplot3_adsr'mplot3': ADSR Plot
mplot3_bar'mplot3': Barplot
mplot3_box'mplot3': Boxplot
mplot3_confPlot confusion matrix
mplot3_confbinPlot extended confusion matrix for binary classification
mplot3_decision'mplot3': Decision boundaries
mplot3_fit'mplot3': True vs. Fitted plot
mplot3_fret'mplot3': Guitar Fretboard
mplot3_graphPlot 'igraph' networks
mplot3_harmonographPlot a harmonograph
mplot3_heatmap'mplot3' Heatmap ('image'; modified 'heatmap')
mplot3_imgDraw image (False color 2D)
mplot3_lateralityLaterality scatter plot
mplot3_lolli'mplot3' Lollipop Plot
mplot3_missingPlot missingness
mplot3_mosaicMosaic plot
mplot3_pr'mplot3' Precision Recall curves
mplot3_prpPlot 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'mplot3' Scatter plot with marginal density and/or histogram
mplot_AGGTEobjPlot AGGTEobj object
mplot_hsvPlot HSV color range
mplot_rasterPlot Array as Raster Image
multigplotMultipanel *ggplot2* plots
nCrn Choose r
nlareg'rtemis' internal: NonLinear Activation regression (NLAreg)
oddsratioCalculate odds ratio for a 2x2 contingency table
oddsratiotableOdds ratio table from logistic regression
oneHotOne hot encoding
onehot2factorConvert one-hot encoded matrix to factor
palettizePalettize colors
permuteCreate permutations
pfreadfread delimited file in parts
plotly.heatHeatmap with 'plotly'
plot.massGAMPlot 'massGAM' object
plot.massGLMPlot 'massGLM' object
plot.resample'plot' method for 'resample' object
plot.rtTestPlot 'rtTest' object
precisionPrecision (aka PPV)
predict.addtreePredict Method for MediBoost Model
predict.boostPredict method for 'boost' object
predict.cartLitePredict method for 'cartLite' object
predict.cartLiteBoostTVPredict method for 'cartLiteBoostTV' object
predict.glmLitePredict method for 'glmLite' object
predict.glmLiteBoostTVPredict method for 'glmLiteBoostTV' object
predict.hytboostPredict method for 'hytboost' object
predict.hytboostnowPredict method for 'hytboostnow' object
predict.hytreenowPredict method for 'hytreeLite' object
predict.hytreewPredict method for 'hytreew' object
predict.LightRuleFit'predict' method for 'LightRuleFit' object
predict.lihadPredict method for 'lihad' object
predict.linadleavesPredict method for 'linadleaves' object
predict.nlaregPredict method for 'nlareg' object
predict.nullmod'rtemis' internal: predict for an object of class 'nullmod'
predict.rtBSplinesPredict S3 method for 'rtBSplines'
predict.rtTLS'predict.rtTLS': 'predict' method for 'rtTLS' object
predict.rulefit'predict' method for 'rulefit' object
preorderlgbPreorder Traversal of LightGBM Tree
preprocessData preprocessing
preprocess_Data preprocessing (in-place)
presentPresent elevate models
present_gridsearchPresent gridsearch results
previewcolorPreview color v2.0
print.addtreePrint method for 'addtree' object created using s_AddTree
print.boostPrint method for boost object
print.cartLiteBoostTVPrint method for cartLiteBoostTV object
print.CheckDataPrint 'CheckData' object
print.class_errorPrint class_error
print.glmLiteBoostTVPrint method for 'glmLiteBoostTV' object
print.gridSearch'print' method for 'gridSearch' object
print.hytboostPrint method for 'hytboost' object
print.hytboostnowPrint method for 'boost' object
print.lihadPrint method for 'lihad' object
print.linadleavesPrint method for 'linadleaves' object
print.massGAM'print'massGAM object
print.massGLM'print'massGLM object
print.regErrorPrint 'regError' object
print.resample'print' method for resample object
print.rtBiasVariancePrint method for bias_variance
print.rtTLS'print.rtTLS': 'print' method for 'rtTLS' object
print.surv_errorPrint surv_error
prune.addtreePrune AddTree tree
psdPopulation Standard Deviation
qstatSGE qstat
readRead tabular data from a variety of formats
recycleRecycle values of vector to match length of target
reluReLU - Rectified Linear Unit
resampleResampling methods
reverseLevelsReverse factor levels
revfactorlevelsReverse factor level order
rfVarSelectVariable Selection by Random Forest
rnormmatRandom Normal Matrix
rowMaxCollapse data.frame to vector by getting row max
rsdCoefficient of Variation (Relative standard deviation)
rsqR-squared
rstudio_theme_rtemisApply rtemis theme for RStudio
rtClust-methodsrtClust S3 methods
rtDecom-methods'print.rtDecom': 'print' method for 'rtDecom' object
rtemis_initInitialize parallel processing and progress reporting
rtemis-package'rtemis': Machine Learning and Visualization
rtemis_paletteAccess rtemis palette colors
rtInitProjectDirInitialize Project Directory
rtlayoutCreate multipanel plots with the 'mplot3' family
rtMeta-methodsrtMeta S3 methods
rtModBag-methodsrtModBag S3 methods
rtModClass-class'rtemis' Classification Model Class
rtModCV-methodsS3 methods for 'rtModCV' class that differ from those of the...
rtModLite-methodsrtModLite S3 methods
rtModLog-class'rtemis' Supervised Model Log Class
rtModLogger-class'rtemis' model logger
rtMod-methods'rtMod' S3 methods
rtpalette'rtemis' Color Palettes
rtPalettesUCSF Colors
rt_reactableView table using reactable
rtROCBuild an ROC curve
rt_saveWrite 'rtemis' model to RDS file
rtset'rtemis' default-setting functions
rtversionGet rtemis and OS version info
rtXDecom-classR6 class for 'rtemis' cross-decompositions
ruleDistRule distance
rules2medmodConvert rules from cutoffs to median/mode and range
runifmatRandom Uniform Matrix
s_AdaBoostAdaboost Binary Classifier C
s_AddTreeAdditive Tree: Tree-Structured Boosting C
savePMMLSave rtemis model to PMML file
s_BARTBayesian Additive Regression Trees (C, R)
s_BayesGLMBayesian GLM
s_BRUTOProjection Pursuit Regression (BRUTO) [R]
s_C50C5.0 Decision Trees and Rule-Based Models C
s_CARTClassification and Regression Trees [C, R, S]
s_CTreeConditional Inference Trees [C, R, S]
seExtract standard error of fit from rtemis model
select_clustSelect 'rtemis' Clusterer
select_decomSelect 'rtemis' Decomposer
selectiterSelect N of learning iterations based on loss
select_learnSelect 'rtemis' Learner
sensitivitySensitivity
seqlSequence generation with automatic cycling
setdiffsymSymmetric Set Difference
setup.bag.resampleSet resample parameters for 'rtMod' bagging
setup.colorSet colorGrad parameters
setup.cv.resample'setup.cv.resample': resample defaults for cross-validation
setup.decomposeSet decomposition parameters for train_cv '.decompose'...
setup.earlystopSet earlystop parameters
setup.GBMSet s_GBM parameters
setup.grid.resampleSet resample parameters for 'gridSearchLearn'
setup.LightRuleFitSet s_LightRuleFit parameters
setup.LIHADSet s_LIHAD parameters
setup.lincoefSet lincoef parameters
setup.MARSSet s_MARS parameters
setup.meta.resampleSet resample parameters for meta model training
setup.preprocessSet preprocess parameters for train_cv '.preprocess' argument
setup.RangerSet s_Ranger parameters
setup.resampleSet resample settings
s_EVTreeEvolutionary Learning of Globally Optimal Trees (C, R)
s_GAMGeneralized Additive Model (GAM) C, R
s_GAM.defaultGeneralized Additive Model (GAM) (C, R)
s_GAM.formulaGeneralized Additive Model (GAM) C, R
s_GBMGradient Boosting Machine [C, R, S]
sge_submitSubmit expression to SGE grid
s_GLMGeneralized Linear Model (C, R)
s_GLMNETGLM with Elastic Net Regularization [C, R, S]
s_GLMTreeGeneralized Linear Model Tree [R]
s_GLSGeneralized Least Squares [R]
s_H2ODLDeep Learning on H2O (C, R)
s_H2OGBMGradient Boosting Machine on H2O (C, R)
s_H2ORFRandom Forest on H2O (C, R)
s_HALHighly Adaptive LASSO [C, R, S]
sigmoidSigmoid function
sizeSize of matrix or vector
s_KNNk-Nearest Neighbors Classification and Regression (C, R)
s_LDALinear Discriminant Analysis
s_LightCARTLightCART Classification and Regression (C, R)
s_LightGBMLightGBM Classification and Regression (C, R)
s_LightRFRandom Forest with LightGBM
s_LightRuleFitRuleFit with LightGBM (C, R)
s_LIHADThe Linear Hard Hybrid Tree: Hard Additive Tree (no gamma)...
s_LIHADBoostBoosting of Linear Hard Additive Trees [R]
s_LINADLinear Additive Tree (C, R)
s_LINOALinear Optimized Additive Tree (C, R)
s_LMLinear model
s_LMTreeLinear Model Tree [R]
s_LOESSLocal Polynomial Regression (LOESS) [R]
s_LOGISTICLogistic Regression
s_MARSMultivariate adaptive regression splines (MARS) (C, R)
s_MLRFSpark MLlib Random Forest (C, R)
s_MULTINOMMultinomial Logistic Regression
s_NBayesNaive Bayes Classifier C
s_NLANonLinear Activation unit Regression (NLA) [R]
s_NLSNonlinear Least Squares (NLS) [R]
s_NWNadaraya-Watson kernel regression [R]
softmaxSoftmax function
softplusSoftplus function
sortedlineslines, but sorted
sparsernormSparse rnorm
sparseVectorSummarySparseness and pairwise correlation of vectors
sparsifySparsify a vector
specificitySpecificity
s_POLYPolynomial Regression
s_PolyMARSMultivariate adaptive polynomial spline regression (POLYMARS)...
s_PPRProjection Pursuit Regression (PPR) [R]
s_PSurvParametric Survival Regression [S]
s_QDAQuadratic Discriminant Analysis C
s_QRNNQuantile Regression Neural Network [R]
s_RangerRandom Forest Classification and Regression (C, R)
s_RFRandom Forest Classification and Regression (C, R)
s_RFSRCRandom Forest for Classification, Regression, and Survival...
s_RLMRobust linear model
s_RuleFitRulefit [C, R]
s_SDASparse Linear Discriminant Analysis
s_SGDStochastic Gradient Descent (SGD) (C, R)
s_SPLSSparse Partial Least Squares Regression (C, R)
s_SVMSupport Vector Machines (C, R)
stderrorStandard Error of the Mean
s_TFNArtificial Neural Network with 'tensorflow' (C, R)
s_TLSTotal Least Squares Regression [R]
strata2factorConvert 'survfit' object's strata to a factor
strat.bootStratified Bootstrap Resampling
strat.subResample using Stratified Subsamples
strictStrict assignment by class or type
strngString formatting utilities
summarizeSummarize numeric variables
summary.massGAM'massGAM' object summary
summary.massGLM'massGLM' object summary
surv_errorSurvival Analysis Metrics
svd1'rtemis-internals' Project Variables to First Eigenvector
s_XGBoostXGBoost Classification and Regression (C, R)
s_XRFXGBoost Random Forest Classification and Regression (C, R)
synth_multimodalCreate "Multimodal" Synthetic Data
synth_reg_dataSynthesize Simple Regression Data
table1Table 1
themeThemes for 'mplot3' and 'dplot3' functions
themesPrint available rtemis themes
timeProcTime a process
tohtmlGenerate 'CheckData' object description in HTML
train_cvTune, Train, and Test an 'rtemis' Learner by Nested...
tunablePrint tunable hyperparameters for a supervised learning...
typesetSet type of columns
uci_heart_failureUCI Heart Failure Data
uniprot_getGet protein sequence from UniProt
uniquevalsperfeatUnique values per feature
winsorizeWinsorize vector
x_CCASparse Canonical Correlation Analysis (CCA)
xlsx2listRead all sheets of an XLSX file into a list
xselect_decomSelect 'rtemis' cross-decomposer
zip2longlatGet Longitude and Lattitude for zip code(s)
zipdistGet distance between pairs of zip codes
egenn/rtemis documentation built on March 28, 2024, 12:53 p.m.