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"name": "R-matlab2r",
"description": "Translation layer from MATLAB to R"
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{
"name": "R-matlib",
"description": "Matrix functions for teaching and learning linear algebra and multivariate statistics"
},
{
"name": "R-matman",
"description": "Material Management"
},
{
"name": "R-Matrix",
"description": "Sparse and dense matrix classes and methods"
},
{
"name": "R-matrixcalc",
"description": "Collection of functions for matrix calculations"
},
{
"name": "R-MatrixCorrelation",
"description": "Matrix Correlation coefficients"
},
{
"name": "R-matrixdist",
"description": "Statistics for matrix distributions"
},
{
"name": "R-MatrixExtra",
"description": "Methods and operators for the sparse matrix classes"
},
{
"name": "R-MatrixGenerics",
"description": "S4 generic summary statistic functions that operate on matrix-like objects"
},
{
"name": "R-MatrixModels",
"description": "Modelling with sparse and dense matrices"
},
{
"name": "R-matrixNormal",
"description": "Matrix normal distribution"
},
{
"name": "R-matrixProfile",
"description": "Matrix Profile"
},
{
"name": "R-matrixprofiler",
"description": "Matrix profile for R"
},
{
"name": "R-matrixsampling",
"description": "Simulations of matrix variate distributions"
},
{
"name": "R-matrixset",
"description": "Creating and manipulating matrix ensemble"
},
{
"name": "R-matrixStats",
"description": "Functions that apply to rows and columns of matrices"
},
{
"name": "R-matrixTests",
"description": "Fast statistical hypothesis tests on rows and columns of matrices"
},
{
"name": "R-matsbyname",
"description": "Implementation of matrix mathematics"
},
{
"name": "R-matsindf",
"description": "Matrices in data frames"
},
{
"name": "R-MaximinInfer",
"description": "Inference for maximin effects in high-dimensional settings"
},
{
"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"
},
{
"name": "R-mbend",
"description": "Matrix Bending"
},
{
"name": "R-MBESS",
"description": "The MBESS R package"
},
{
"name": "R-mblm",
"description": "Median-Based Linear Models"
},
{
"name": "R-mboost",
"description": "Model-based boosting"
},
{
"name": "R-mbrglm",
"description": "Median Bias Reduction in Binomial-response GLMs"
},
{
"name": "R-MBSP",
"description": "Multivariate Bayesian model with shrinkage priors"
},
{
"name": "R-mbsts",
"description": "Multivariate Bayesian Structural Time Series"
},
{
"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"
},
{
"name": "R-MCCM",
"description": "Mixed Correlation Coefficient Matrix"
},
{
"name": "R-mcemGLM",
"description": "Maximum likelihood estimation for generalized linear mixed models"
},
{
"name": "R-mcen",
"description": "Multivariate Cluster Elastic Net"
},
{
"name": "R-mcga",
"description": "Machine coded genetic algorithms for real-valued optimization problems"
},
{
"name": "R-mcgf",
"description": "Markov Chain Gaussian Fields simulation and parameter estimation"
},
{
"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"
}
]
}