development

Maximum Likelihood Conjoint Measurement

Classification Models with Copula Functions

Collection of Machine Learning Datasets for Supervised Machine Learning

Exploratory Data Analysis and Manipulation of Multi-Label Data Sets

Maximum Likelihood Difference Scaling

Maximum Likelihood Estimation of Various Univariate and Multivariate Distributions

Machine Learning Algorithms with Unified Interface and Confusion Matrices

Asymptotic Efficient Closed-Form Estimators for Multivariate Distributions

Computation of the MLE for Bivariate Interval Censored Data

Maximum Likelihood Estimates of Gaussian Processes

MLE for Normally Distributed Data Censored by Limit of Detection

Multilevel Exponential-Family Random Graph Models

Machine Learning Model Evaluation

Machine Learning Experiments

Machine Learning Foundations

Iterative Proportional Fitting Algorithms for Nested Structures

Machine Learning Forest Simulator

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

Multi-Layer Group-Lasso

Multilevel Index of Dissimilarity

Single and Multiple Imputation with Automated Machine Learning

R6-Based ML Learners for 'mlexperiments'

Multilevel Mediation Analysis

Multi-Level Monte Carlo

Machine Learning Evaluation Metrics

Multilevel/Mixed Model Helper Functions

Maximum Likelihood Multiple Imputation

Maximum Likelihood Estimation of DNA Methylation and Hydroxymethylation Proportions

Model Selection in Multivariate Longitudinal Data Analysis

Estimating Frequencies, Prevalence and Multiplicity of Infection

Power Analysis and Data Simulation for Multilevel Models

Examples from Multilevel Modelling Software Review

Multilevel Monitoring System Data for Wells in the USGS INL Aquifer Monitoring Network

Multi-Level Model Assessment Kit

Machine Learning Algorithms for Multivariate Time Series

Practical Multilevel Modeling

Multinomial Logit Models

Bayesian Model Averaging for Multinomial Logit Models

'Rcpp' Integration for the 'mlpack' Library

Maximum Likelihood Estimation of the Niche Preemption Model

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

A Power Analysis Toolbox to Find Cost-Efficient Study Designs

Algorithms for Class Distribution Estimation

Machine Learning in R

Machine Learning in R - Next Generation

Batch Experiments for 'mlr3'

Analysis and Visualisation of Benchmark Experiments

Cluster Extension for 'mlr3'

Collection of Machine Learning Data Sets for 'mlr3'

Data Base Backend for 'mlr3'

Fairness Auditing and Debiasing for 'mlr3'

Extending 'mlr3' to Functional Data Analysis

Filter Based Feature Selection for 'mlr3'

Feature Selection for 'mlr3'

Hyperband for 'mlr3'

Inference on the Generalization Error

Recommended Learners for 'mlr3'

Flexible Bayesian Optimization

Performance Measures for 'mlr3'

Helper Functions for 'mlr3'

Preprocessing Operators and Pipelines for 'mlr3'

Resampling Algorithms for 'mlr3' Framework

Machine Learning in 'shiny' with 'mlr3'

Support for Spatial Objects Within the 'mlr3' Ecosystem

Model and Learner Summaries for 'mlr3'

Super Learner Fitting and Prediction

Hyperparameter Optimization for 'mlr3'

Search Spaces for 'mlr3'

Easily Install and Load the 'mlr3' Package Family

Visualizations for 'mlr3'

Composable Preprocessing Operators and Pipelines for Machine Learning

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

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

Stepwise Regression with Assumptions Checking

Long-Run Variance Estimation in Time Series Regression

Efficient Estimation of Bayesian SBMs & MLSBMs

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

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