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r-miceadds
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public |
Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
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2024-01-16 |
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r-minqa
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None |
Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.
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2024-01-16 |
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r-minpack.lm
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public |
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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2024-01-16 |
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r-minerva
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public |
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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2024-01-16 |
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r-mhurdle
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public |
Estimation of models with zero left-censored variables. Null values may be caused by a selection process Cragg (1971) <doi:10.2307/1909582>, insufficient resources Tobin (1958) <doi:10.2307/1907382> or infrequency of purchase Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.
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2024-01-16 |
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r-mined
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public |
This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) <DOI:10.1080/00401706.2018.1552203>. 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. This research is supported by a U.S. National Science Foundation grant DMS-1712642.
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2024-01-16 |
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r-mime
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None |
Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems.
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2024-01-16 |
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r-microseq
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public |
Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.
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2024-01-16 |
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r-microbenchmark
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None |
Provides infrastructure to accurately measure and compare the execution time of R expressions.
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2024-01-16 |
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r-mice
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public |
Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
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2024-01-16 |
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r-micefast
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public |
Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. 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. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.
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2024-01-16 |
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r-mev
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public |
Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
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2024-01-16 |
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r-mets
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public |
Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Also contains two-stage binomial modelling that can do pairwise odds-ratio dependence modelling based marginal logistic regression models. This is an alternative to the alternating logistic regression approach (ALR).
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2024-01-16 |
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r-mhsmm
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public |
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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2024-01-16 |
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r-mhtmult
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public |
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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2024-01-16 |
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r-mgsda
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public |
Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA <doi:10.1080/01621459.2015.1034318>.
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2024-01-16 |
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r-mgcv
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None |
Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.
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2024-01-16 |
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r-mgl
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public |
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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2024-01-16 |
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r-mgarchbekk
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public |
Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and multivariate GJR (bivariate asymmetric GARCH model) specification.
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2024-01-16 |
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r-mfgarch
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public |
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 (2020, <doi:10.1002/jae.2742>). 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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2024-01-16 |
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r-metabma
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public |
Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, <doi:10.1177/25152459211031256>).
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2024-01-16 |
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r-mewavg
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public |
Compute the average of a sequence of random vectors in a moving expanding window using a fixed amount of memory.
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2024-01-16 |
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r-metacoder
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public |
A set of tools for parsing, manipulating, and graphing data classified by a hierarchy (e.g. a taxonomy).
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2024-01-16 |
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r-meteor
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public |
A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modeling, particularly crop and crop disease modeling.
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2024-01-16 |
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r-metafolio
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public |
A tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper Anderson et al. (2015) <doi:10.1101/2022.03.24.485545>.
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2024-01-16 |
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r-metadynminer
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public |
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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2024-01-16 |
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r-mess
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public |
A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book.
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2024-01-16 |
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r-mendelianrandomization
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public |
Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.
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2024-01-16 |
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r-memisc
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public |
An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.
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2024-01-16 |
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r-melt
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public |
Performs multiple empirical likelihood tests for linear and generalized linear models. The package offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the 'Eigen' C++ library and 'RcppEigen' interface, with OpenMP for parallel computation. Details of the testing procedures are given in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.
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2024-01-16 |
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r-mergetrees
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public |
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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2024-01-16 |
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r-memuse
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public |
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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2024-01-16 |
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r-memo
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public |
A simple in-memory, LRU cache that can be wrapped around any function to memoize it. The cache is keyed on a hash of the input data (using 'digest') or on pointer equivalence.
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2024-01-16 |
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r-meanshiftr
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public |
Performs mean shift classification using linear and k-d tree based nearest neighbor implementations for the Gaussian, Epanechnikov, and biweight product kernels.
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2024-01-16 |
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r-meboot
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public |
Maximum entropy density based dependent data bootstrap. An algorithm is provided to create a population of time series (ensemble) without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI: 10.1016/j.jempfin.2003.06.002>) explains how the algorithm satisfies the ergodic theorem and the central limit theorem.
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2024-01-16 |
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r-mdmb
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public |
Contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2020a, 2020b; <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.
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2024-01-16 |
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r-mediak
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public |
Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.
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2024-01-16 |
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r-mcmcglmm
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public |
Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).
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2024-01-16 |
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r-meanr
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public |
Sentiment analysis is a popular technique in text mining that attempts to determine the emotional state of some text. We provide a new implementation of a common method for computing sentiment, whereby words are scored as positive or negative according to a dictionary lookup. Then the sum of those scores is returned for the document. We use the 'Hu' and 'Liu' sentiment dictionary ('Hu' and 'Liu', 2004) <doi:10.1145/1014052.1014073> for determining sentiment. The scoring function is 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'.
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2024-01-16 |
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r-mboost
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public |
Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.
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2024-01-16 |
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r-mdfs
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public |
Functions for MultiDimensional Feature Selection (MDFS): calculating multidimensional information gains, scoring variables, finding important variables, plotting selection results. This package includes an optional CUDA implementation that speeds up information gain calculation using NVIDIA GPGPUs. R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.
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2024-01-16 |
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r-mdendro
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public |
A comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. Multidendrograms can group more than two clusters when ties in proximity data occur, and therefore they do not depend on the order of the input data. Descriptive measures to analyze the resulting dendrogram are additionally provided.
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2024-01-16 |
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r-mda
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public |
Mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines. Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York.
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2024-01-16 |
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r-mcr
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public |
Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. It implements the CLSI recommendations (see J. A. Budd et al. (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) for analytical method comparison and bias estimation using patient samples. Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>). A comprehensive overview over the implemented methods and references can be found in the manual pages "mcr-package" and "mcreg".
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2024-01-16 |
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r-mco
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public |
A collection of function to solve multiple criteria optimization problems using genetic algorithms (NSGA-II). Also included is a collection of test functions.
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2024-01-16 |
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r-mcmcse
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public |
Provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE computation for expectation and quantile estimators is supported as well as multivariate estimations. The package also provides functions for computing effective sample size and for plotting Monte Carlo estimates versus sample size.
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2024-01-16 |
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r-mcmcprecision
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public |
Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.
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2024-01-16 |
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r-mcmcpack
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public |
Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.
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2024-01-16 |
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r-mbrglm
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public |
Fit generalized linear models with binomial responses using a median modified score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to median bias reduction. This method respects equivariance under reparameterizations for each parameter component and also solves the infinite estimates problem (data separation).
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2024-01-16 |
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r-mclust
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public |
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
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2024-01-16 |
