All packages

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missingHE — 1.5.0

Missing Outcome Data in Health Economic Evaluation

MissingPlotLSD — 0.1.0

Missing Plot in LSD

MissingPlotRBD — 1.1.0

Missing Plot in RBD

missMDA — 1.19

Handling Missing Values with Multivariate Data Analysis

MissMech — 1.0.4

Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random

missMethods — 0.4.0

Methods for Missing Data

missoNet — 1.2.0

Missingness in Multi-Task Regression with Network Estimation

misspi — 0.1.0

Missing Value Imputation in Parallel

Missplot — 0.1.0

Missing Plot Technique in Design of Experiment

missRanger — 2.6.0

Fast Imputation of Missing Values

missSBM — 1.0.4

Handling Missing Data in Stochastic Block Models

missSOM — 1.0.1

Self-Organizing Maps with Built-in Missing Data Imputation

mistr — 0.0.6

Mixture and Composite Distributions

mistral — 2.2.2

Methods in Structural Reliability

misty — 0.6.8

Miscellaneous Functions 'T. Yanagida'

misuvi — 0.1.0

Access the Michigan Substance Use Vulnerability Index (MI-SUVI)

mitml — 0.4-5

Tools for Multiple Imputation in Multilevel Modeling

MitoHEAR — 0.1.0

Quantification of Mitochondrial DNA Heteroplasmy

mitools — 2.4

Tools for Multiple Imputation of Missing Data

mitre — 1.0.0

Cybersecurity MITRE Standards Data and Digraphs

MittagLeffleR — 0.4.1

Mittag-Leffler Family of Distributions

miWQS — 0.4.4

Multiple Imputation Using Weighted Quantile Sum Regression

mix — 1.0-12

Estimation/Multiple Imputation for Mixed Categorical and Continuous Data

mixAK — 5.8

Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

MixAll — 1.5.16

Clustering and Classification using Model-Based Mixture Models

mixAR — 0.22.8

Mixture Autoregressive Models

mixbox — 1.2.3

Observed Fisher Information Matrix for Finite Mixture Model

mixcat — 1.0-4

Mixed Effects Cumulative Link and Logistic Regression Models

mixchar — 0.1.0

Mixture Model for the Deconvolution of Thermal Decay Curves

mixcure — 2.0

Mixture Cure Models

mixdir — 0.3.0

Cluster High Dimensional Categorical Datasets

mixdist — 0.5-5

Finite Mixture Distribution Models

mixedBayes — 0.1.3

Bayesian Longitudinal Regularized Quantile Mixed Model

mixedCCA — 1.6.2

Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data

MixedIndTests — 1.2.0

Tests of Randomness and Tests of Independence

MixedLevelRSDs — 1.0.0

Mixed Level Response Surface Designs

mixedLSR — 0.1.0

Mixed, Low-Rank, and Sparse Multivariate Regression on High-Dimensional Data

mixedMem — 1.1.2

Tools for Discrete Multivariate Mixed Membership Models

MixedPoisson — 2.0

Mixed Poisson Models

MixedPsy — 1.1.0

Statistical Tools for the Analysis of Psychophysical Data

mixedsde — 5.0

Estimation Methods for Stochastic Differential Mixed Effects Models

MixedTS — 1.0.4

Mixed Tempered Stable Distribution

mixexp — 1.2.7.1

Design and Analysis of Mixture Experiments

MIXFIM — 1.1

Evaluation of the FIM in NLMEMs using MCMC

MixfMRI — 0.1-4

Mixture fMRI Clustering Analysis

mixgb — 1.0.2

Multiple Imputation Through 'XGBoost'

MixGHD — 2.3.7

Model Based Clustering, Classification and Discriminant Analysis Using the Mixture of Generalized Hyperbolic Distributions

mixhvg — 1.0.1

Mixture of Multiple Highly Variable Feature Selection Methods

mixIndependR — 1.0.0

Genetics and Independence Testing of Mixed Genetic Panels

mixKernel — 0.9-1

Omics Data Integration Using Kernel Methods

mixl — 1.3.4

Simulated Maximum Likelihood Estimation of Mixed Logit Models for Large Datasets

MixLFA — 1.0.0

Mixture of Longitudinal Factor Analysis Methods

mixlm — 1.4.0

Mixed Model ANOVA and Statistics for Education

MixMatrix — 0.2.8

Classification with Matrix Variate Normal and t Distributions

mixmeta — 1.2.0

An Extended Mixed-Effects Framework for Meta-Analysis

mixOofA — 1.0

Design and Analysis of Order-of-Addition Mixture Experiments

mixopt — 0.1.3

Mixed Variable Optimization

MixOptim — 0.1.2

Mixture Optimization Algorithm

mixPHM — 0.7-2

Mixtures of Proportional Hazard Models

mixpoissonreg — 1.0.0

Mixed Poisson Regression for Overdispersed Count Data

mixR — 0.2.1

Finite Mixture Modeling for Raw and Binned Data

mixRaschTools — 1.1.1

Plotting and Average Theta Functions for Multiple Class Mixed Rasch Models

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 — 0.9.2

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

Tools for Analyzing Finite Mixture Models

mixtox — 1.4.0

Dose Response Curve Fitting and Mixture Toxicity Assessment

mixtur — 1.2.1

Modelling Continuous Report Visual Short-Term Memory Studies

mixture — 2.1.1

Mixture Models for Clustering and Classification

MixtureMissing — 3.0.3

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 — 0.3

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 — 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 — 2.1-5

Machine Learning Benchmark Problems

mlbplotR — 1.1.0

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

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