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Package website: release | dev

Meta-package for installing and using core mlr3 packages.

mlr3verse

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Overview

This package simplifies installing and loading packages from the mlr3 ecosystem. Instead of attaching extension packages directly, this package re-exports commonly used functions for data analysis and provides a lightweight interface to core mlr3 ecosystem functionality.

Installation

# From CRAN:
install.packages("mlr3verse")

# From GitHub:
pak::pak("mlr-org/mlr3verse")

What’s included

Functions and objects from the following packages are imported by this meta-package:

Name Title URL
mlr3 Machine Learning in R - Next Generation https://mlr3.mlr-org.com
mlr3cluster Unsupervised Clustering https://mlr3cluster.mlr-org.com
mlr3data Additional data sets and tasks https://mlr3data.mlr-org.com
mlr3filters Filter Based Feature Selection https://mlr3filters.mlr-org.com
mlr3fselect Wrapper Based Feature Selection https://mlr3fselect.mlr-org.com
mlr3learners Recommended Learners https://mlr3learners.mlr-org.com
mlr3pipelines Preprocessing Operators and Pipelines https://mlr3pipelines.mlr-org.com
mlr3torch Deep Learning https://mlr3torch.mlr-org.com
mlr3tuning Hyperparameter Tuning https://mlr3tuning.mlr-org.com
mlr3tuningspaces Collection of Hyperparameter Tuning Spaces https://mlr3tuningspaces.mlr-org.com
mlr3viz Visualizations https://mlr3viz.mlr-org.com
paradox Parameter Spaces https://paradox.mlr-org.com

After loading mlr3verse, you are ready to work on most regression, classification, clustering and survival tasks:

library("mlr3verse")

For more detailed information about loaded packages, call mlr3verse_info():

mlr3verse_info()

You can install additional packages with:

install.packages("mlr3verse", dependencies = TRUE)
Name Title URL
miesmuschel Mixed Integer Evolution Strategies
mlr3batchmark Batch Experiments https://mlr3batchmark.mlr-org.com
mlr3benchmark Analysis and Visualization of Benchmark Experiments https://mlr3benchmark.mlr-org.com
mlr3db Database Backend https://mlr3db.mlr-org.com
mlr3fairness Fairness Auditing and Debiasing https://mlr3fairness.mlr-org.com
mlr3fda Functional Data Analysis https://mlr3fda.mlr-org.com
mlr3forecast Time Series Forecasting https://mlr3forecast.mlr-org.com
mlr3oml OpenML Integration https://mlr3oml.mlr-org.com
mlr3proba Probabilistic Supervised Learning https://mlr3proba.mlr-org.com
mlr3spatial Spatial Data Analysis https://mlr3spatial.mlr-org.com
mlr3spatiotempcv Spatiotemporal Resampling Methods https://mlr3spatiotempcv.mlr-org.com
mlr3summary Model and Learner Summaries https://mlr3summary.mlr-org.com
mlr3extralearners Extra Learners https://mlr3extralearners.mlr-org.com
rush Decentralized and Distributed Computing https://rush.mlr-org.com

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.