README.md
In egenn/rtemis: Machine Learning and Visualization

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 and vignettes

R version 4.1 or higher

You can install rtemis from r-universe or using pak, remotes, or devtools.

r-universe:

r install.packages('rtemis', repos = c('https://egenn.r-universe.dev', 'https://cloud.r-project.org'))

pak:

r pak::pkg_install("egenn/rtemis")

remotes:

r remotes::install_github("egenn/rtemis")

devtools:

r devtools::install_github("egenn/rtemis")

To allow compilation from source of any dependencies that require Fortran, you will need to install the GNU Fortran compiler. The easiest way to do this is with Homebrew:

brew install gcc

Then, you will need to add the following to your ~/.R/Makevars file:

FC      = usr/local/opt/gcc/bin/gfortran
F77     = /usr/local/opt/gcc/bin/gfortran
FLIBS   = -L/usr/local/opt/gcc/lib

Note about using `d_UMAP()`

d_UMAP() requires the uwot package, which currently requires that the Matrix and irlba dependencies be installed from source. See more in the uwot issue here.

See here for more setup and installation instructions.

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

Get cross-validated random forest performance on the iris dataset:

library(rtemis)
mod <- train_cv(iris)

We are working towards the 1.0 release, which will feature updates to the API as well as the backend, and preparing for CRAN submission. This will be accompanied by expansion of the documentation. For all updates, please see the NEWS file.

The Python and Julia ports, rtemispy and Rtemis.jl, which are not yet publicly available, are in parallel development. With the upcoming 1.0 release of rtemis, the aim is to offer a unified API across all three languages.

Visualization
Static: mplot3 family (base graphics)
Dynamic: dplot3 family (plotly)
Unsupervised Learning
Clustering: c*_
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)

rtemislive is rtemis' web interface / GUI. It makes advanced visualization and modeling instantly accessible by all. It is currently available for beta testing at UCSF, and will be made publicly available once funding is secured for a hosting server.