A platform for advanced Machine Learning research and applications.
The goal of rtemis is to make data science efficient and accessible with no compromise on flexibility.

• Online documentation and vignettes
• R PDF manual

See here for more setup and installation instructions.

install.packages("remotes")
remotes::install_github("egenn/rtemis")

Note: Make sure to keep your installation updated by running remotes::install_github("egenn/rtemis") regularly: it will only proceed if there are updates available

Install dependencies if they are not already installed:

packages <- c("pbapply", "ranger")
.add <- !packages %in% installed.packages()
install.packages(packages[.add])

Load rtemis and get cross-validated random forest performance on the iris dataset:

library(rtemis)
mod <- elevate(iris)
mod$plot()

• v0.79: 07-02-2019 "Super Papaya" Release out
• v0.78: 04-02-2019 rtemis moved to public repo

• Visualization

  • Static: mplot3 family (base graphics)
  • Dynamic: dplot3 family (plotly)

• Unsupervised Learning

  • Clustering: u.*
  • Decomposition: d.*

• Supervised Learning

  • Classification, Regression, Survival Analysis: s.*

• Cross-Decomposition

  • Sparse Canonical Correlation / Sparse Decomposition: x.*

• Meta-Models

  • Model Stacking: metaMod()
  • Modality Stacking: metaFeat()
  • Group-weighted Stacking: metaGroup()

  (metaFeat and metaGroup have been temporarily removed for updating)

• Novel algorithms developed in rtemis will generally be added to this public repository as soon as the corresponding papers or preprints are published.
• R Documentation is ongoing and should be completed soon.
• rtemis is under active development with many enhancements and extensions in the works

2019 Efstathios (Stathis) D. Gennatas MBBS AICSM PhD