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rtemis Machine Learning and Visualization Build Status

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

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

10-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

  • v0.79: 07-02-2019 "Super Papaya" Release out
  • v0.78: 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 temporarily removed for updating)

Ongoing work

  • 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