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r-multivator
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A multivariate generalization of the emulator package.
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2025-04-22 |
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r-multivarsel
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It performs variable selection in a multivariate linear model by estimating the covariance matrix of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion. The method is described in the paper Perrot-Dockès et al. (2017) <arXiv:1704.00076>.
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2025-04-22 |
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r-multisv
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MultiSV is an R package for identification of structural variations in multiple populations based on whole genome resequencing. It fits linear mixed model and identifies structural variations in multiple populations using whole genome sequencing data. It could also be manipulated to use on RNA-seq data for differential gene expression (implementation in future releases). Main steps for analysis include generating read depth in bins using ComputeBinCounts. conversion of bins to MultiSV format using Bin2MultiSV. Finally, identification of structural variations using CallMultiSV.
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2025-04-22 |
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r-multisom
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Implements two versions of the algorithm namely: stochastic and batch. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
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2025-04-22 |
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r-multirr
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Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
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2025-04-22 |
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r-multiroc
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Tools to solve real-world problems with multiple classes classifications by computing the areas under ROC and PR curve via micro-averaging and macro-averaging. The vignettes of this package can be found via <https://github.com/WandeRum/multiROC>. The methodology is described in V. Van Asch (2013) <https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et al. (2011) <http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.
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2025-04-22 |
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r-multirobust
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Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.
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2025-04-22 |
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r-multirng
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Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.
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2025-04-22 |
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r-multirich
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Functions to calculate Unique Trait Combinations (UTC) and scaled Unique Trait Combinations (sUTC) as measures of multivariate richness. The package can also calculate beta-diversity for trait richness and can partition this into nestedness-related and turnover components. The code will also calculate several measures of overlap.
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2025-04-22 |
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r-multirdpg
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Fits the Multiple Random Dot Product Graph Model and performs a test for whether two networks come from the same distribution. Both methods are proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational and Graphical Statistics).
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2025-04-22 |
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r-multipol
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Various utilities to manipulate multivariate polynomials.
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2025-04-22 |
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r-multiplex
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Algebraic procedures for the analysis of multiple social networks are delivered with this package. Among other things, it makes possible to create and manipulate multivariate network data with different formats, and there are effective ways available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup or the semiring structure together with the relational bundles occurring in different types of multivariate network data sets. It also provides an algebraic approach for two-mode networks through Galois derivations between families of the pairs of subsets in the two domains.
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2025-04-22 |
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r-multiplencc
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Fit Cox proportional hazard models with a weighted partial likelihood. It handles one or multiple endpoints, additional matching and makes it possible to reuse controls for other endpoints.
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2025-04-22 |
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r-multiplebubbles
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Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.
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2025-04-22 |
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r-multiord
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A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006).
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2025-04-22 |
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r-multinomialci
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An implementation of a method for building simultaneous confidence intervals for the probabilities of a multinomial distribution given a set of observations, proposed by Sison and Glaz in their paper: Sison, C.P and J. Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 90:366-369 (1995). The method is an R translation of the SAS code implemented by May and Johnson in their paper: May, W.L. and W.D. Johnson. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000). Paper and code available at <DOI:10.18637/jss.v005.i06>.
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2025-04-22 |
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r-multinbmod
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This is a likelihood approach for the regression analysis of overdispersed correlated count data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.
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2025-04-22 |
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r-multilevel
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The functions in this package are designed to be used in the analysis of multilevel data by applied psychologists. The package includes functions for estimating common within-group agreement and reliability indices. The package also contains basic data manipulation functions that facilitate the analysis of multilevel and longitudinal data.
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2025-04-22 |
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r-multilaterals
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Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.
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2025-04-22 |
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r-multijoin
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Wrapper around the Unix join facility which is more efficient than the built-in R routine merge(). The package enables the joining of multiple files on disk at once. The files can be compressed and various filters can be deployed before joining. Compiles only under Unix.
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2025-04-22 |
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r-multigroup
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Several functions are presented in order to study data in a group structure, where the same set of variables are measured on different groups of individuals.
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2025-04-22 |
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r-multifwf
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Read a table of fixed width formatted data of different types into a data.frame for each type.
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2025-04-22 |
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r-multicoll
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The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed. D.A. Belsley (1982) <doi:10.1016/0304-4076(82)90020-3>. D. A. Belsley (1991, ISBN: 978-0471528890). C. Garcia, R. Salmeron and C.B. Garcia (2019) <doi:10.1080/00949655.2018.1543423>. R. Salmeron, C.B. Garcia and J. Garcia (2018) <doi:10.1080/00949655.2018.1463376>. G.W. Stewart (1987) <doi:10.1214/ss/1177013444>.
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2025-04-22 |
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r-multicmp
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A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.
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2025-04-22 |
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r-multica
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Implements a generalization of the Cochran-Armitage trend test to multinomial data. In addition to an overall test, multiple testing adjusted p-values for trend in individual outcomes and power calculation is available.
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2025-04-22 |
