All packages

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MixRF — 1.0

A Random-Forest-Based Approach for Imputing Clustered Incomplete Data

MixSAL — 1.0

Mixtures of Multivariate Shifted Asymmetric Laplace (SAL) Distributions

MixSemiRob — 1.1.0

Mixture Models: Parametric, Semiparametric, and Robust

MixSIAR — 3.1.12

Bayesian Mixing Models in R

MixSim — 1.1-8

Simulating Data to Study Performance of Clustering Algorithms

mixsmsn — 1.1-10

Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions

mixSPE — 0.9.3

Mixtures of Power Exponential and Skew Power Exponential Distributions for Use in Model-Based Clustering and Classification

mixsqp — 0.3-54

Sequential Quadratic Programming for Fast Maximum-Likelihood Estimation of Mixture Proportions

mixSSG — 2.1.1

Clustering Using Mixtures of Sub Gaussian Stable Distributions

mixtools — 2.0.0.1

Tools for Analyzing Finite Mixture Models

mixtox — 1.4.0

Dose Response Curve Fitting and Mixture Toxicity Assessment

mixtree — 0.0.1

A Statistical Framework for Comparing Sets of Trees

mixtur — 1.2.1

Modelling Continuous Report Visual Short-Term Memory Studies

mixture — 2.1.1

Mixture Models for Clustering and Classification

MixtureMissing — 3.0.4

Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random

MixTwice — 2.0

Large-Scale Hypothesis Testing by Variance Mixing

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 — 2.5.3

Dynamic Multi-Species Size Spectrum Modelling

MJMbamlss — 0.1.0

Multivariate Joint Models with 'bamlss'

MKclass — 0.5

Statistical Classification

mkde — 0.4

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 — 1.2.9

Kinetic Evaluation of Chemical Degradation Data

MKinfer — 1.2

Inferential Statistics

MKLE — 1.0.1

Maximum Kernel Likelihood Estimation

MKMeans — 3.1

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 — 1.0

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 — 2.1-6

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 — 1.0.1

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 — 1.5

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 — 0.1-7.1

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 — 0.0.5

Machine Learning Experiments

mlf — 1.2.1

Machine Learning Foundations

mlfit — 0.5.3

Iterative Proportional Fitting Algorithms for Nested Structures

mlflow — 2.21.3

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 — 0.0.5

R6-Based ML Learners for 'mlexperiments'

mlma — 6.3-1

Multilevel Mediation Analysis

mlmc — 2.1.1

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 — 1.0.9

Power Analysis and Data Simulation for Multilevel Models

mlmRev — 1.0-8

Examples from Multilevel Modelling Software Review

mlms — 1.0.2

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

mlmtools — 1.0.2

Multi-Level Model Assessment Kit

mlmts — 1.1.2

Machine Learning Algorithms for Multivariate Time Series

MLMusingR — 0.4.0

Practical Multilevel Modeling

mlogit — 1.1-1

Multinomial Logit Models

mlogitBMA — 0.1-9

Bayesian Model Averaging for Multinomial Logit Models

mlpack — 4.6.0

'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 — 1.1.1

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 — 0.23.0

Machine Learning in R - Next Generation

mlr3batchmark — 0.2.0

Batch Experiments for 'mlr3'

mlr3benchmark — 0.1.7

Analysis and Visualisation of Benchmark Experiments

mlr3cluster — 0.1.11

Cluster Extension for 'mlr3'

mlr3data — 0.9.0

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 — 0.8.1

Filter Based Feature Selection for 'mlr3'

mlr3fselect — 1.3.0

Feature Selection for 'mlr3'

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