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"name": "R-matrixStats",
"description": "Functions that apply to rows and columns of matrices"
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{
"name": "R-matrixTests",
"description": "Fast statistical hypothesis tests on rows and columns of matrices"
},
{
"name": "R-matsbyname",
"description": "Implementation of matrix mathematics"
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{
"name": "R-matsindf",
"description": "Matrices in data frames"
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{
"name": "R-MaximinInfer",
"description": "Inference for maximin effects in high-dimensional settings"
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{
"name": "R-maxLik",
"description": "Maximum likelihood estimation and related tools"
},
{
"name": "R-maxlike",
"description": "Model species distributions by estimating the probability of occurrence using presence-only data"
},
{
"name": "R-maxstat",
"description": "Maximally selected rank statistics with several p-value approximations"
},
{
"name": "R-maybe",
"description": "The Maybe monad"
},
{
"name": "R-MBA",
"description": "Multilevel b-spline approximation"
},
{
"name": "R-mbbefd",
"description": "Maxwell–Boltzmann–Bose–Einstein–Fermi–Dirac Distribution and destruction rate modelling"
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{
"name": "R-mbend",
"description": "Matrix Bending"
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{
"name": "R-MBESS",
"description": "The MBESS R package"
},
{
"name": "R-mblm",
"description": "Median-Based Linear Models"
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{
"name": "R-mboost",
"description": "Model-based boosting"
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{
"name": "R-mbrglm",
"description": "Median Bias Reduction in Binomial-response GLMs"
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{
"name": "R-MBSP",
"description": "Multivariate Bayesian model with shrinkage priors"
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{
"name": "R-mbsts",
"description": "Multivariate Bayesian Structural Time Series"
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{
"name": "R-mBvs",
"description": "Bayesian variable selection methods for multivariate data"
},
{
"name": "R-mc2d",
"description": "Tools for two-dimensional Monte-Carlo simulations"
},
{
"name": "R-mcauchyd",
"description": "Multivariate Cauchy Distribution, Kullback–Leibler divergence"
},
{
"name": "R-mcclust",
"description": "Process an MCMC sample of clusterings"
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{
"name": "R-MCCM",
"description": "Mixed Correlation Coefficient Matrix"
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{
"name": "R-mcemGLM",
"description": "Maximum likelihood estimation for generalized linear mixed models"
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{
"name": "R-mcen",
"description": "Multivariate Cluster Elastic Net"
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{
"name": "R-mcga",
"description": "Machine coded genetic algorithms for real-valued optimization problems"
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{
"name": "R-mcgf",
"description": "Markov Chain Gaussian Fields simulation and parameter estimation"
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{
"name": "R-mcgibbsit",
"description": "Warnes and Raftery’s MCGibbsit MCMC run length and convergence diagnostic"
},
{
"name": "R-mcglm",
"description": "Multivariate covariance generalized linear models"
},
{
"name": "R-mclogit",
"description": "Multinomial logit models"
},
{
"name": "R-mclust",
"description": "Gaussian mixture modelling for model-based clustering, classification and density estimation"
},
{
"name": "R-mclustcomp",
"description": "Measures for comparing clusters"
},
{
"name": "R-mcmc",
"description": "Markov Chain Monte Carlo"
},
{
"name": "R-MCMCglmm",
"description": "MCMC generalised linear mixed models"
},
{
"name": "R-MCMCpack",
"description": "Markov Chain Monte Carlo (MCMC) package"
},
{
"name": "R-mcmcplots",
"description": "Functions for convenient plotting and viewing of MCMC output"
},
{
"name": "R-MCMCprecision",
"description": "Precision of discrete parameters in transdimensional MCMC"
},
{
"name": "R-mcmcr",
"description": "Manipulate MCMC samples"
},
{
"name": "R-mcmcsae",
"description": "Markov Chain Monte Carlo Small Area Estimation"
},
{
"name": "R-mcmcse",
"description": "Monte Carlo standard errors for MCMC"
},
{
"name": "R-MCMCvis",
"description": "Tools to visualize, manipulate and summarize MCMC output"
},
{
"name": "R-mco",
"description": "Multiple-criteria optimization algorithms and related functions"
},
{
"name": "R-Mcomp",
"description": "Data from the M-competitions"
},
{
"name": "R-mcompanion",
"description": "Objects and methods for multi-companion matrices"
},
{
"name": "R-mcp",
"description": "Regression with multiple change points"
},
{
"name": "R-MCPAN",
"description": "Multiple contrast tests and simultaneous confidence intervals based on normal approximation"
},
{
"name": "R-mcr",
"description": "Method Comparison Regression"
},
{
"name": "R-mcunit",
"description": "Unit testing for Monte Carlo methods"
},
{
"name": "R-mda",
"description": "Mixture and flexible discriminant analysis"
},
{
"name": "R-mDAG",
"description": "Inferring causal network from mixed observational data using a directed acyclic graph"
}
]
}