All packages

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MMDvariance — 0.0.9

Detecting Differentially Variable Genes Using the Mixture of Marginal Distributions

mme — 0.1-6

Multinomial Mixed Effects Models

mmeln — 1.5

Estimation of Multinormal Mixture Distribution

MMeM — 0.1.1

Multivariate Mixed Effects Model

MMGFM — 1.1.0

Multi-Study Multi-Modality Generalized Factor Model

mmibain — 0.2.0

Bayesian Informative Hypotheses Evaluation Web Applications

mmiCATs — 0.2.0

Cluster Adjusted t Statistic Applications

MMINP — 0.1.0

Microbe-Metabolite Interactions-Based Metabolic Profiles Predictor

mmints — 0.1.0

Workflows for Building Web Applications

mmirestriktor — 0.3.1

Informative Hypothesis Testing Web Applications

MMLR — 0.2.0

Fitting Markov-Modulated Linear Regression Models

mmmgee — 1.20

Simultaneous Inference for Multiple Linear Contrasts in GEE Models

MMOC — 0.1.1.0

Multi-Omic Spectral Clustering using the Flag Manifold

mmod — 1.3.3

Modern Measures of Population Differentiation

mmodely — 0.2.5

Modeling Multivariate Origins Determinants - Evolutionary Lineages in Ecology

mMPA — 1.2.0

Implementation of Marker-Assisted Mini-Pooling with Algorithm

mmpca — 2.0.3

Integrative Analysis of Several Related Data Matrices

mmpp — 0.6

Various Similarity and Distance Metrics for Marked Point Processes

mmr — 0.1.0

Matrix Multiplication on Data.frames

MMRcaseselection — 0.1.0

Case Classification and Selection Based on Regression Results

mmrm — 0.3.13

Mixed Models for Repeated Measures

mmsample — 0.1

Multivariate Matched Sampling

mmstat4 — 0.2.1

Access to Teaching Materials from a ZIP File or GitHub

mmtsne — 0.1.0

Multiple Maps t-SNE

MMVBVS — 0.8.0

Missing Multivariate Bayesian Variable Selection

MMWRweek — 0.1.3

Convert Dates to MMWR Day, Week, and Year

MN — 1.0

Matrix Normal Distribution

MNARclust — 1.1.0

Clustering Data with Non-Ignorable Missingness using Semi-Parametric Mixture Models

MNB — 1.1.0

Diagnostic Tools for a Multivariate Negative Binomial Model

mnda — 1.0.9

Multiplex Network Differential Analysis (MNDA)

mnet — 0.1.2

Modeling Group Differences and Moderation Effects in Statistical Network Models

mnis — 0.3.1

Easy Downloading Capabilities for the Members' Name Information Service

mniw — 1.0.2

The Matrix-Normal Inverse-Wishart Distribution

mnlfa — 0.3-4

Moderated Nonlinear Factor Analysis

MNLpred — 0.0.8

Simulated Predicted Probabilities for Multinomial Logit Models

MNLR — 0.1.0

Interactive Shiny Presentation for Working with Multinomial Logistic Regression

MNM — 1.0-4

Multivariate Nonparametric Methods. An Approach Based on Spatial Signs and Ranks

mnonr — 1.0.0

A Generator of Multivariate Non-Normal Random Numbers

mnorm — 1.2.2

Multivariate Normal Distribution

mnormt — 2.1.1

The Multivariate Normal and t Distributions, and Their Truncated Versions

MNP — 3.1-5

Fitting the Multinomial Probit Model

mnreadR — 2.1.7

MNREAD Parameters Estimation and Curve Plotting

MNS — 1.0

Mixed Neighbourhood Selection

mnt — 1.3

Affine Invariant Tests of Multivariate Normality

mob — 0.4.2

Monotonic Optimal Binning

mobilityIndexR — 0.2.1

Calculates Transition Matrices and Mobility Indices

MoBPS — 1.6.64

Modular Breeding Program Simulator

mobr — 3.0.0

Measurement of Biodiversity

mobsim — 0.3.1

Spatial Simulation and Scale-Dependent Analysis of Biodiversity Changes

moc.gapbk — 0.1.3

Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge

MOCCA — 1.4

Multi-Objective Optimization for Collecting Cluster Alternatives

MOCHA — 1.1.0

Modeling for Single-Cell Open Chromatin Analysis

mockery — 0.4.4

Mocking Library for R

mockr — 0.2.1

Mocking in R

mockthat — 0.2.8

Function Mocking for Unit Testing

mod — 0.1.3

Lightweight and Self-Contained Modules for Code Organization

mod09nrt — 0.14

Extraction of Bands from MODIS Surface Reflectance Product MOD09 NRT

mod2rm — 0.2.1

Moderation Analysis for Two-Instance Repeated Measures Designs

modACDC — 2.0.1

Association of Covariance for Detecting Differential Co-Expression

Modalclust — 0.7

Hierarchical Modal Clustering

modeest — 2.4.0

Mode Estimation

modehunt — 1.0.7

Multiscale Analysis for Density Functions

model4you — 0.9-7

Stratified and Personalised Models Based on Model-Based Trees and Forests

modelbased — 0.8.8

Estimation of Model-Based Predictions, Contrasts and Means

modelbpp — 0.1.5

Model BIC Posterior Probability

modelc — 1.0.0.0

A Linear Model to 'SQL' Compiler

Modelcharts — 0.1.0

Classification Model Charts

modeldata — 1.4.0

Data Sets Useful for Modeling Examples

modeldatatoo — 0.3.0

More Data Sets Useful for Modeling Examples

modeldb — 0.3.0

Fits Models Inside the Database

modelDown — 1.1

Make Static HTML Website for Predictive Models

modelenv — 0.1.1

Provide Tools to Register Models for Use in 'tidymodels'

Modeler — 3.4.5

Classes and Methods for Training and Using Binary Prediction Models

modelfactory — 1.0.0

Combine Statistical Models into a Tibble for Comparison

modelfree — 1.2

Model-Free Estimation of a Psychometric Function

modelgrid — 1.2.0

A Framework for Creating, Managing and Training Multiple 'caret' Models

modelimpact — 1.0.0

Functions to Assess the Business Impact of Churn Prediction Models

modeLLtest — 1.0.4

Compare Models with Cross-Validated Log-Likelihood

ModelMap — 3.4.0.4

Modeling and Map Production using Random Forest and Related Stochastic Models

ModelMatrixModel — 0.1.0

Create Model Matrix and Save the Transforming Parameters

ModelMetrics — 1.2.2.2

Rapid Calculation of Model Metrics

modelObj — 4.2

A Model Object Framework for Regression Analysis

modelr — 0.1.11

Modelling Functions that Work with the Pipe

modelSSE — 0.1-3

Modelling Infectious Disease Superspreading from Contact Tracing Data

modelStudio — 3.1.2

Interactive Studio for Explanatory Model Analysis

modelsummary — 2.2.0

Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready

modeltests — 0.1.6

Testing Infrastructure for Broom Model Generics

modeltime — 1.3.0

The Tidymodels Extension for Time Series Modeling

modeltime.ensemble — 1.0.4

Ensemble Algorithms for Time Series Forecasting with Modeltime

modeltime.resample — 0.2.3

Resampling Tools for Time Series Forecasting

modeltools — 0.2-23

Tools and Classes for Statistical Models

modelwordcloud — 0.1

Model Word Clouds

moder — 0.2.1

Mode Estimation

moderate.mediation — 0.0.10

Causal Moderated Mediation Analysis

moderndive — 0.7.0

Tidyverse-Friendly Introductory Linear Regression

modernVA — 0.1.3

An Implementation of Two Modern Education-Based Value-Added Models

modest — 0.3-1

Model-Based Dose-Escalation Trials

ModEstM — 0.0.1

Mode Estimation, Even in the Multimodal Case

modesto — 0.1.4

Modeling and Analysis of Stochastic Systems

modEvA — 3.18.2

Model Evaluation and Analysis

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