This guide describes the rtemis 1.4.2 API. rtemis requires R 4.4.0 or later. Algorithm backends are installed according to the methods used in an analysis.
The R-universe package page lists the available build. Use the development source for APIs ahead of that build.
BART: Bayesian Additive Regression Trees (stochtree)
CART: CART decision tree (rpart)
ConditionalSuperLearner: Conditional SuperLearner: an oracle routes each case to one of a library of experts
GAM: GAM (generalized additive model)
GLM: GLM (generalized linear model)
GLMNET: Elastic net (glmnet)
GLMTree: Model-Based Recursive Partitioning: a tree with a GLM in each leaf
HAL: Highly Adaptive Lasso (hal9001)
Isotonic: Isotonic regression
MonotonicHAL: Monotonic Highly Adaptive Lasso (hal9001)
KNN: k-Nearest Neighbors (kknn)
LightCART: Single LightGBM tree (CART mode)
LightGBM: LightGBM gradient boosting
LightRF: LightGBM random forest
LightRuleFit: LightRuleFit (LightGBM rules + GLMNET)
LINAD: Linear Additive Tree
LINADForest: Bagged ensemble of Linear Additive Trees
MARS: Multivariate Adaptive Regression Splines (earth)
MLP: Multilayer perceptron (torch)
ModalityStacking: ModalityStacking: stacked ensemble with one learner per feature group
NNLS: Non-negative least squares (nnls)
Ranger: Ranger random forest
SuperLearner: SuperLearner: cross-validated stacked ensemble
LinearSVM: SVM with linear kernel (e1071)
RadialSVM: SVM with radial kernel (e1071)
SPLS: Sparse Partial Least Squares
TabNet: TabNet neural network
The first model uses CART through the rpart package. Other backends include ranger, glmnet, lightgbm, and torch. Neural-network methods require a working torch runtime in addition to the R package. Backend requirements appear in the corresponding setup function’s help.
