All packages

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mlf — 1.2.1

Machine Learning Foundations

mlfit — 0.5.3

Iterative Proportional Fitting Algorithms for Nested Structures

mlflow — 2.16.2

Interface to 'MLflow'

MLFS — 0.4.2

Machine Learning Forest Simulator

MLGdata — 0.1.0

Datasets for Use with Salvan, Sartori and Pace (2020)

MLGL — 1.0.0

Multi-Layer Group-Lasso

MLID — 1.0.1

Multilevel Index of Dissimilarity

mlim — 0.3.0

Single and Multiple Imputation with Automated Machine Learning

mllrnrs — 0.0.4

R6-Based ML Learners for 'mlexperiments'

mlma — 6.3-1

Multilevel Mediation Analysis

mlmc — 2.0.2

Multi-Level Monte Carlo

MLmetrics — 1.1.3

Machine Learning Evaluation Metrics

mlmhelpr — 0.1.1

Multilevel/Mixed Model Helper Functions

mlmi — 1.1.2

Maximum Likelihood Multiple Imputation

MLML2R — 0.3.3

Maximum Likelihood Estimation of DNA Methylation and Hydroxymethylation Proportions

mlmm.gwas — 1.0.6

Pipeline for GWAS Using MLMM

MLModelSelection — 1.0

Model Selection in Multivariate Longitudinal Data Analysis

MLMOI — 0.1.2

Estimating Frequencies, Prevalence and Multiplicity of Infection

mlmpower — 1.0.8

Power Analysis and Data Simulation for Multilevel Models

mlmRev — 1.0-8

Examples from Multilevel Modelling Software Review

mlmtools — 1.0.2

Multi-Level Model Assessment Kit

mlmts — 1.1.2

Machine Learning Algorithms for Multivariate Time Series

MLMusingR — 0.3.2

Practical Multilevel Modeling

mlogit — 1.1-1

Multinomial Logit Models

mlogitBMA — 0.1-7

Bayesian Model Averaging for Multinomial Logit Models

mlpack — 4.4.0

'Rcpp' Integration for the 'mlpack' Library

MLpreemption — 1.0.1

Maximum Likelihood Estimation of the Niche Preemption Model

MLPUGS — 0.2.0

Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)

mlpwr — 1.1.0

A Power Analysis Toolbox to Find Cost-Efficient Study Designs

mlquantify — 0.2.0

Algorithms for Class Distribution Estimation

mlr — 2.19.2

Machine Learning in R

mlr3 — 0.21.0

Machine Learning in R - Next Generation

mlr3batchmark — 0.1.1

Batch Experiments for 'mlr3'

mlr3benchmark — 0.1.6

Analysis and Visualisation of Benchmark Experiments

mlr3cluster — 0.1.9

Cluster Extension for 'mlr3'

mlr3data — 0.7.0

Collection of Machine Learning Data Sets for 'mlr3'

mlr3db — 0.5.2

Data Base Backend for 'mlr3'

mlr3fairness — 0.3.2

Fairness Auditing and Debiasing for 'mlr3'

mlr3fda — 0.2.0

Extending 'mlr3' to Functional Data Analysis

mlr3filters — 0.8.0

Filter Based Feature Selection for 'mlr3'

mlr3fselect — 1.1.0

Feature Selection for 'mlr3'

mlr3hyperband — 0.6.0

Hyperband for 'mlr3'

mlr3learners — 0.7.0

Recommended Learners for 'mlr3'

mlr3mbo — 0.2.4

Flexible Bayesian Optimization

mlr3measures — 1.0.0

Performance Measures for 'mlr3'

mlr3misc — 0.15.1

Helper Functions for 'mlr3'

mlr3oml — 0.10.0

Connector Between 'mlr3' and 'OpenML'

mlr3pipelines — 0.6.0

Preprocessing Operators and Pipelines for 'mlr3'

mlr3resampling — 2024.9.6

Resampling Algorithms for 'mlr3' Framework

mlr3shiny — 0.3.0

Machine Learning in 'shiny' with 'mlr3'

mlr3spatial — 0.5.0

Support for Spatial Objects Within the 'mlr3' Ecosystem

mlr3spatiotempcv — 2.3.1

Spatiotemporal Resampling Methods for 'mlr3'

mlr3summary — 0.1.0

Model and Learner Summaries for 'mlr3'

mlr3superlearner — 0.1.2

Super Learner Fitting and Prediction

mlr3torch — 0.1.1

Deep Learning with 'mlr3'

mlr3tuning — 1.0.1

Hyperparameter Optimization for 'mlr3'

mlr3tuningspaces — 0.5.1

Search Spaces for 'mlr3'

mlr3verse — 0.3.0

Easily Install and Load the 'mlr3' Package Family

mlr3viz — 0.9.0

Visualizations for 'mlr3'

mlrCPO — 0.3.7-7

Composable Preprocessing Operators and Pipelines for Machine Learning

mlrintermbo — 0.5.1-1

Model-Based Optimization for 'mlr3' Through 'mlrMBO'

mlrMBO — 1.1.5.1

Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions

mlrpro — 0.1.2

Stepwise Regression with Assumptions Checking

mlrv — 0.1.2

Long-Run Variance Estimation in Time Series Regression

mlsbm — 0.99.2

Efficient Estimation of Bayesian SBMs & MLSBMs

mlsjunkgen — 0.1.2

Use the MLS Junk Generator Algorithm to Generate a Stream of Pseudo-Random Numbers

mlstrOpalr — 1.0.3

Support Compatibility Between 'Maelstrom' R Packages and 'Opal' Environment

mlsurvlrnrs — 0.0.4

R6-Based ML Survival Learners for 'mlexperiments'

mlt — 1.6-0

Most Likely Transformations

mlt.docreg — 1.1-9

Most Likely Transformations: Documentation and Regression Tests

mltest — 1.0.1

Classification Evaluation Metrics

mltools — 0.3.5

Machine Learning Tools

mlts — 1.0.0

Multilevel Latent Time Series Models with 'R' and 'Stan'

mlVAR — 0.5.2

Multi-Level Vector Autoregression

MLVSBM — 0.2.4

A Stochastic Block Model for Multilevel Networks

mlxR — 4.2.0

Simulation of Longitudinal Data

MLZ — 0.1.4

Mean Length-Based Estimators of Mortality using TMB

MM — 1.6-8

The Multiplicative Multinomial Distribution

MM2Sdata — 1.0.3

Gene Expression Datasets for the 'MM2S' Package

MM4LMM — 3.0.2

Inference of Linear Mixed Models Through MM Algorithm

mma — 10.7-1

Multiple Mediation Analysis

mmabig — 3.2-0

Multiple Mediation Analysis for Big Data Sets

MMAC — 0.1.2

Data for Mathematical Modeling and Applied Calculus

MMAD — 1.0.0

MM Algorithm Based on the Assembly-Decomposition Technology

mmand — 1.6.3

Mathematical Morphology in Any Number of Dimensions

mmap — 0.6-22

Map Pages of Memory

mmapcharr — 0.3.0

Memory-Map Character Files

mmaqshiny — 1.0.0

Explore Air-Quality Mobile-Monitoring Data

mMARCH.AC — 2.9.4.0

Processing of Accelerometry Data with 'GGIR' in mMARCH

mmb — 0.13.3

Arbitrary Dependency Mixed Multivariate Bayesian Models

mmc — 0.0.3

Multivariate Measurement Error Correction

mmcards — 0.1.1

Playing Cards Utility Functions

mmcif — 0.1.1

Mixed Multivariate Cumulative Incidence Functions

mmcm — 1.2-8

Modified Maximum Contrast Method

mmconvert — 0.10

Mouse Map Converter

Mmcsd — 1.0.0

Modeling Complex Longitudinal Data in a Quick and Easy Way

MMD — 1.0.0

Minimal Multilocus Distance (MMD) for Source Attribution and Loci Selection

MMDai — 2.0.0

Multivariate Multinomial Distribution Approximation and Imputation for Incomplete Categorical Data

MMDCopula — 0.2.1

Robust Estimation of Copulas by Maximum Mean Discrepancy

MMDvariance — 0.0.9

Detecting Differentially Variable Genes Using the Mixture of Marginal Distributions

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