All packages

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MixviR — 3.5.0

Analysis and Exploration of Mixed Microbial Genomic Samples

mixvlmc — 0.2.1

Variable Length Markov Chains with Covariates

mize — 0.2.4

Unconstrained Numerical Optimization Algorithms

mizer — 2.5.1

Dynamic Multi-Species Size Spectrum Modelling

MJMbamlss — 0.1.0

Multivariate Joint Models with 'bamlss'

MKclass — 0.5

Statistical Classification

mkde — 0.3

2D and 3D Movement-Based Kernel Density Estimates (MKDEs)

MKdescr — 0.8

Descriptive Statistics

MKendall — 1.5-4

Matrix Kendall's Tau and Matrix Elliptical Factor Model

mkin — 1.2.6

Kinetic Evaluation of Chemical Degradation Data

MKinfer — 1.2

Inferential Statistics

MKLE — 1.0.1

Maximum Kernel Likelihood Estimation

MKMeans — 2.1

A Modern K-Means (MKMeans) Clustering Algorithm

MKmisc — 1.9

Miscellaneous Functions from M. Kohl

mknapsack — 0.1.0

Multiple Knapsack Problem Solver

MKomics — 0.7

Omics Data Analysis

MKpower — 0.9

Power Analysis and Sample Size Calculation

mkssd — 1.2

Efficient Multi-Level k-Circulant Supersaturated Designs

ML.MSBD — 1.2.1

Maximum Likelihood Inference on Multi-State Trees

ML2Pvae — 1.0.0.1

Variational Autoencoder Models for IRT Parameter Estimation

mlapi — 0.1.1

Abstract Classes for Building 'scikit-learn' Like API

mlbench — 2.1-5

Machine Learning Benchmark Problems

mlbplotR — 1.1.0

Create 'ggplot2' and 'gt' Visuals with Major League Baseball Logos

mlbstats — 0.1.0

Major League Baseball Player Statistics Calculator

MLCIRTwithin — 2.1.1

Latent Class Item Response Theory (LC-IRT) Models under Within-Item Multidimensionality

MLCM — 0.4.3

Maximum Likelihood Conjoint Measurement

MLCOPULA — 1.0.0

Classification Models with Copula Functions

MLDataR — 1.0.1

Collection of Machine Learning Datasets for Supervised Machine Learning

mldr — 0.4.3

Exploratory Data Analysis and Manipulation of Multi-Label Data Sets

mldr.datasets — 0.4.2

R Ultimate Multilabel Dataset Repository

mldr.resampling — 0.2.3

Resampling Algorithms for Multi-Label Datasets

MLDS — 0.5.1

Maximum Likelihood Difference Scaling

MLE — 1.1

Maximum Likelihood Estimation of Various Univariate and Multivariate Distributions

mle.tools — 1.0.0

Expected/Observed Fisher Information and Bias-Corrected Maximum Likelihood Estimate(s)

mlearning — 1.2.1

Machine Learning Algorithms with Unified Interface and Confusion Matrices

MLEce — 2.1.0

Asymptotic Efficient Closed-Form Estimators for Multivariate Distributions

MLEcens — 0.1-7

Computation of the MLE for Bivariate Interval Censored Data

mlegp — 3.1.9

Maximum Likelihood Estimates of Gaussian Processes

mlelod — 1.0.0.1

MLE for Normally Distributed Data Censored by Limit of Detection

mlergm — 0.8

Multilevel Exponential-Family Random Graph Models

MLeval — 0.3

Machine Learning Model Evaluation

mlexperiments — 0.0.4

Machine Learning Experiments

mlf — 1.2.1

Machine Learning Foundations

mlfit — 0.5.3

Iterative Proportional Fitting Algorithms for Nested Structures

mlflow — 2.14.1

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 — 0.20.2

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

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