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r-modelltest
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An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.
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2025-03-25 |
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r-modelgood
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Bootstrap cross-validation for ROC, AUC and Brier score to assess and compare predictions of binary status responses.
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2025-03-25 |
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r-moc
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Fits and vizualize user defined finite mixture models for multivariate observations using maximum likelihood. (McLachlan, G., Peel, D. (2000) Finite Mixture Models. Wiley-Interscience.)
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2025-03-25 |
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r-mnp
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Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). ``A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation,'' Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. <DOI:10.1016/j.jeconom.2004.02.002> Detailed examples are given in Imai and van Dyk (2005). ``MNP: R Package for Fitting the Multinomial Probit Model.'' Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. <DOI:10.18637/jss.v014.i03>.
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2025-03-25 |
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r-mnormpow
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Computes integral of f(x)*x_i^k on a product of intervals, where f is the density of a gaussian law. This a is small alteration of the mnormt code from A. Genz and A. Azzalini.
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2025-03-25 |
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r-mmsample
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Subset a control group to match an intervention group on a set of features using multivariate matching and propensity score calipers. Based on methods in Rosenbaum and Rubin (1985).
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2025-03-25 |
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r-mmeta
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A novel multivariate meta-analysis.
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2025-03-25 |
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r-mmap
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R interface to POSIX mmap and Window's MapViewOfFile.
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2025-03-25 |
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r-mmand
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Provides tools for performing mathematical morphology operations, such as erosion and dilation, on data of arbitrary dimensionality. Can also be used for finding connected components, resampling, filtering, smoothing and other image processing-style operations.
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2025-03-25 |
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r-mm4lmm
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The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Hunter, D. R., & Lange, K. (2004) <doi:10.1198/0003130042836>).
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2025-03-25 |
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r-mlr3misc
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Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
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2025-03-25 |
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r-mlmmm
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Computational strategies for multivariate linear mixed-effects models with missing values, Schafer and Yucel (2002), Journal of Computational and Graphical Statistics, 11, 421-442.
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2025-03-25 |
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r-mlegp
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Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>. Contact the maintainer for a package version that includes sensitivity analysis.
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2025-03-25 |
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r-mlecens
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This package contains functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set 'cosmesis'), and for censored data with competing risks (see data set 'menopause'). We also provide functions to visualize the observed data and the MLE.
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2025-03-25 |
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r-mlbench
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A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.
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2025-03-25 |
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r-mkde
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Provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.
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2025-03-25 |
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r-mixtureregltic
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Fit mixture regression models with nonsusceptibility/cure for left-truncated and interval-censored (LTIC) data (see Chen et al. (2013) <doi:10.1002/sim.5845>). This package also provides the nonparametric maximum likelihood estimator (NPMLE) for the survival/event curves with LTIC data.
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2025-03-25 |
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r-mixture
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An implementation of all 14 Gaussian parsimonious clustering models (GPCMs) for model-based clustering and model-based classification.
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2025-03-25 |
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r-mixsqp
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Provides optimization algorithms based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithms are expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly in large data sets. The algorithms are described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2018) <arXiv:1806.01412>.
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2025-03-25 |
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r-mixsim
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The utility of this package is in simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of 'MixSim', there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models.
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2025-03-25 |
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r-mixor
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Provides the function 'mixor' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
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2025-03-25 |
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r-mixmatrix
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Provides sampling and density functions for matrix variate normal, t, and inverted t distributions; ML estimation for matrix variate normal and t distributions using the EM algorithm, including some restrictions on the parameters; and classification by linear and quadratic discriminant analysis for matrix variate normal and t distributions described in Thompson et al. (2019) <arXiv:1907.09565>. Performs clustering with matrix variate normal and t mixture models.
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2025-03-25 |
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r-mixedmem
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Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.
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2025-03-25 |
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r-mixeddataimpute
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Missing data imputation for continuous and categorical data, using nonparametric Bayesian joint models (specifically the hierarchically coupled mixture model with local dependence described in Murray and Reiter (2015); see 'citation("MixedDataImpute")' or http://arxiv.org/abs/1410.0438). See '?hcmm_impute' for example usage.
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2025-03-25 |
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r-mix
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Estimation/multiple imputation programs for mixed categorical and continuous data.
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2025-03-25 |
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r-misssbm
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When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM' adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in Tabouy, Barbillon and Chiquet (2019) <doi:10.1080/01621459.2018.1562934>.
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2025-03-25 |
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r-mirada
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This package collects algorithms/functions developed for microRNA profiling data analyses. Analytical platforms include traditional hybridization microarray, CGH, beads-based microarray, and qRT-PCR array.
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2025-03-25 |
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r-minpack.lm
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The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds. The implementation can be used via nls-like calls using the nlsLM function.
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2025-03-25 |
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r-minerva
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Wrapper for 'minepy' implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of 'minepy' can be found at <http://minepy.readthedocs.io/en/latest>.
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2025-03-25 |
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r-mined
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This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2018). The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate.
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2025-03-25 |
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r-microseq
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Basic functions for microbial sequence data analysis. The idea is to use the basic R data structures as much as possible, without building complex data types.
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2025-03-25 |
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r-micefast
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Fast imputations under the object-oriented programming paradigm. There was used quantitative models with a closed-form solution. Thus package is based on linear algebra operations. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
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2025-03-25 |
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r-mhtmult
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A Comprehensive tool for almost all existing multiple testing methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.
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2025-03-25 |
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r-mht
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Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.
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2025-03-25 |
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r-mhsmm
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Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences. Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
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2025-03-25 |
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r-mgsda
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Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2015), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA, to appear,[DOI:10.1080/01621459.2015.1034318].
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2025-03-25 |
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r-mgl
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An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961.
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2025-03-25 |
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r-mgarchbekk
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Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and multivariate GJR (bivariate asymmetric GARCH model) specification.
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2025-03-25 |
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r-mfgarch
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Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2018, <doi:10.2139/ssrn.2752354>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.
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2025-03-25 |
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r-mewavg
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Computes the average of a sequence of random vectors in a moving expanding window using a fixed amount of storage
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2025-03-25 |
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r-meteor
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A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modelling, particularly crop and crop disease modeling.
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2025-03-25 |
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r-metaheuristicfpa
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A nature-inspired metaheuristics algorithm based on the pollination process of flowers. This R package makes it easy to implement the standard flower pollination algorithm for every user. The algorithm was first developed by Xin-She Yang in 2012 (<DOI:10.1007/978-3-642-32894-7_27>).
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2025-03-25 |
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r-metafolio
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The metafolio R package is a tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper 'Portfolio conservation of metapopulations under climate change'. See citation("metafolio").
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2025-03-25 |
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r-metadynminer
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Metadynamics is a state of the art biomolecular simulation technique. 'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037> to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Transition states can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H. (2000) <doi:10.1063/1.1323224>. Free energy surfaces, minima and transition paths can be plotted to produce publication quality images.
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2025-03-25 |
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r-mergetrees
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Aggregates a set of trees with the same leaves to create a consensus tree. The trees are typically obtained via hierarchical clustering, hence the hclust format is used to encode both the aggregated trees and the final consensus tree. The method is exact and proven to be O(nqlog(n)), n being the individuals and q being the number of trees to aggregate.
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2025-03-25 |
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r-memuse
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How much ram do you need to store a 100,000 by 100,000 matrix? How much ram is your current R session using? How much ram do you even have? Learn the scintillating answer to these and many more such questions with the 'memuse' package.
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2025-03-25 |
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r-memo
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A simple in-memory, LRU cache that can be wrapped around any function to memoize it. The cache can be keyed on a hash of the input data (using 'digest') or on pointer equivalence.
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2025-03-25 |
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r-memnet
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Efficient implementations of network science tools to facilitate research into human (semantic) memory. In its current version, the package contains several methods to infer networks from verbal fluency data, various network growth models, diverse (switcher-) random walk processes, and tools to analyze and visualize networks. To deliver maximum performance the majority of the code is written in C++. For an application see: Wulff, D. U., Hills, T., & Mata, R. (2018) <doi:10.31234/osf.io/s73dp>.
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2025-03-25 |
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r-mediak
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Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.
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2025-03-25 |
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r-v8
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An R interface to Google's open source JavaScript engine. This package can now be compiled either with V8 version 6 or 7 (LTS) from nodejs or with the legacy 3.14/3.15 branch of V8.
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2025-03-25 |
