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.
R version 4.1 or higher
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()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.
07-02-2019: "Super Papaya" Release out
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 removed for updating)
- 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.
