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r-modeldata
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Data sets used for demonstrating or testing model-related packages are contained in this package.
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2025-04-22 |
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r-modeest
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Provides estimators of the mode of univariate data or univariate distributions.
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2025-04-22 |
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r-modelbased
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Implements a general interface for model-based estimations for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), used in the computation of marginal means, contrast analysis and predictions.
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2025-04-22 |
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r-mockthat
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With the deprecation of mocking capabilities shipped with 'testthat' as of 'edition 3' it is left to third-party packages to replace this functionality, which in some test-scenarios is essential in order to run unit tests in limited environments (such as no Internet connection). Mocking in this setting means temporarily substituting a function with a stub that acts in some sense like the original function (for example by serving a HTTP response that has been cached as a file). The only exported function 'with_mock()' is modeled after the eponymous 'testthat' function with the intention of providing a drop-in replacement.
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2025-04-22 |
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r-mmod
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Provides functions for measuring population divergence from genotypic data.
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2025-04-22 |
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r-mlvar
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Estimates the multi-level vector autoregression model on time-series data. Three network structures are obtained: temporal networks, contemporaneous networks and between-subjects networks.
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2025-04-22 |
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r-mlt.docreg
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Additional documentation, a package vignette and regression tests for package mlt.
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2025-04-22 |
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r-mlr3viz
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Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the 'autoplot()' generic of 'ggplot2'. The package draws plots with the 'viridis' color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves.
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2025-04-22 |
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r-mlr3spatiotempcv
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Extends the mlr3 ML framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored.
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2025-04-22 |
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r-mlr3tuningspaces
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Collection of search spaces for hyperparameter optimization in the 'mlr3' ecosystem. It features ready-to-use search spaces for many popular machine learning algorithms. The search spaces are from scientific articles and work for a wide range of data sets.
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2025-04-22 |
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r-mlr3tuning
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Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling.
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2025-04-22 |
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r-mlr3verse
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The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at <https://mlr3book.mlr-org.com/>.
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2025-04-22 |
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r-mlr3pipelines
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Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.
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2025-04-22 |
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r-mlr3measures
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Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are.
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2025-04-22 |
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r-mlr3learners
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Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.
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2025-04-22 |
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r-mlr3filters
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Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported.
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2025-04-22 |
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r-mlr3hyperband
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Successive Halving (Jamieson and Talwalkar (2016) <arXiv:1502.07943>) and Hyperband (Li et al. 2018 <arXiv:1603.06560>) optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk.
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2025-04-22 |
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r-mlr3fselect
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Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.
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2025-04-22 |
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r-mlr3data
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A small collection of interesting and educational machine learning data sets which are used as examples in the 'mlr3' book (<https://mlr3book.mlr-org.com>), the use case gallery (<https://mlr3gallery.mlr-org.com>), or in other examples. All data sets are properly preprocessed and ready to be analyzed by most machine learning algorithms. Data sets are automatically added to the dictionary of tasks if 'mlr3' is loaded.
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2025-04-22 |
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r-mlr3cluster
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Extends the 'mlr3' package with cluster analysis.
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2025-04-22 |
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r-mlr3
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Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.
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2025-04-22 |
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r-mldr
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Exploratory data analysis and manipulation functions for multi- label data sets along with an interactive Shiny application to ease their use.
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2025-04-22 |
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r-mlflow
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R interface to 'MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models.
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2025-04-22 |
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r-mleval
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Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.
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2025-04-22 |
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r-mkpower
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Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>), comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>), and ANCOVA (Shieh (2020), <doi:10.1007/s11336-019-09692-3>).
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2025-04-22 |
