Package website: release | dev
Meta-package for installing and using core mlr3 packages.
This package is intended to simplify both installation and loading of packages from the mlr3 ecosystem. Instead of depending on the extension packages, functions required for data analysis are re-exported, providing a thin view on the most important functionality of the mlr3 ecosystem.
# From CRAN:
install.packages("mlr3verse")
# From Github:
remotes::install_github("mlr-org/mlr3verse")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 |
| 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 |
By loading the mlr3verse package, you are all set to deal with most
regression, classification, cluster and survival tasks:
library("mlr3verse")
#> Loading required package: mlr3If you want to get more detailed information about the loaded packages,
you can call mlr3verse_info():
mlr3verse_info()Additional packages can be installed 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 Visualisation 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 |
| mlr3oml | OpenML Integration | https://mlr3oml.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 | |
| mlr3torch | Deep Learning | https://mlr3torch.mlr-org.com |
| rush | Decentralized and Distributed Computing | https://rush.mlr-org.com |
