rtemis Machine Learning and Visualization

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.

Documentation

• Documentation and vignettes

Installation

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

60-second intro to rtemis

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()

What's new

0.80.0 Beta

An accumulation of updates and added functionality, algorithms, graphics.
Majority of mplot3 and dplot3 functions now work with the new theme system provided by theme_* functions like theme_lightgrid and theme_darkgrid.

0.79

07-02-2019: "Super Papaya" Release out

0.78

04-02-2019: rtemis moved to public repo

Features

• 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 removed for updating)

Ongoing work

• rtemis is under active development
• Novel algorithms developed in rtemis will generally be added to this public repository around the publication of the corresponding papers.
• R Documentation is ongoing.

2020 Efstathios (Stathis) D. Gennatas MBBS AICSM PhD