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r-mcmc
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public |
Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>, function morph.metrop), which achieves geometric ergodicity by change of variable.
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2024-01-16 |
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r-mattransmix
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public |
Provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations.
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2024-01-16 |
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r-mcclust
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public |
Implements methods for processing a sample of (hard) clusterings, e.g. the MCMC output of a Bayesian clustering model. Among them are methods that find a single best clustering to represent the sample, which are based on the posterior similarity matrix or a relabelling algorithm.
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2024-01-16 |
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r-mbvs
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public |
Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data (Lee et al., Biometrics, 2017 <doi:10.1111/biom.12557>) and zero-inflated count data (Lee et al., Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).
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2024-01-16 |
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r-matrixextra
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public |
Extends sparse matrix and vector classes from the 'Matrix' package by providing: (a) Methods and operators that work natively on CSR formats (compressed sparse row, a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); (b) Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, including the 'float32' type from 'float'; (c) Coercion methods between pairs of classes which are not present in 'Matrix', such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; (d) Utility functions for sparse matrices such as sorting the indices or removing zero-valued entries; (e) Fast transposes that work by outputting in the opposite storage format; (f) Faster replacements for many 'Matrix' methods for all sparse types, such as slicing and elementwise multiplication. (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.
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2024-01-16 |
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r-maxstat
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public |
Maximally selected rank statistics with several p-value approximations.
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2024-01-16 |
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r-mbest
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public |
Fast moment-based hierarchical model fitting. Implements methods from the papers "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017) and "Fitting a Deeply Nested Hierarchical Model to a Large Book Review Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry (2018).
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2024-01-16 |
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r-mba
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public |
Functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by Øyvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).
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2024-01-16 |
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r-matchit
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public |
Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)
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2024-01-16 |
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r-maxpro
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public |
Generate maximum projection (MaxPro) designs for quantitative and/or qualitative factors. Details of the MaxPro criterion can be found in: (1) Joseph, Gul, and Ba. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika, 102, 371-380, and (2) Joseph, Gul, and Ba. (2018) "Designing Computer Experiments with Multiple Types of Factors: The MaxPro Approach", Journal of Quality Technology, to appear.
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2024-01-16 |
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r-matrix
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None |
A rich hierarchy of sparse and dense matrix classes, including general, symmetric, triangular, and diagonal matrices with numeric, logical, or pattern entries. Efficient methods for operating on such matrices, often wrapping the 'BLAS', 'LAPACK', and 'SuiteSparse' libraries.
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2024-01-16 |
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r-matrixstats
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High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
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2024-01-16 |
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r-marked
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public |
Functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. There are also Hidden Markov model (HMM) implementations of CJS and multistate models with and without state uncertainty and a simulation capability for HMM models.
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2024-01-16 |
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r-mathjaxr
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Provides 'MathJax' and macros to enable its use within Rd files for rendering equations in the HTML help files.
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2024-01-16 |
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r-markovchain
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public |
Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depends on the suggested ctmcd package, that, as May 2023, can be retrieved from <https://cran.r-project.org/src/contrib/Archive/ctmcd/ctmcd_1.4.2.tar.gz>.
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2024-01-16 |
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r-marginaleffects
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public |
Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference.
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2024-01-16 |
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r-matchingr
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public |
Computes matching algorithms quickly using Rcpp. Implements the Gale-Shapley Algorithm to compute the stable matching for two-sided markets, such as the stable marriage problem and the college-admissions problem. Implements Irving's Algorithm for the stable roommate problem. Implements the top trading cycle algorithm for the indivisible goods trading problem.
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2024-01-16 |
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r-matching
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public |
Provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, <doi:10.18637/jss.v042.i07>).
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2024-01-16 |
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r-mat
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public |
Simulates Multidimensional Adaptive Testing using the multidimensional three-parameter logistic model as described in Segall (1996) <doi:10.1007/BF02294343>, van der Linden (1999) <doi:10.3102/10769986024004398>, Reckase (2009) <doi:10.1007/978-0-387-89976-3>, and Mulder & van der Linden (2009) <doi:10.1007/s11336-008-9097-5>.
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2024-01-16 |
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r-mass
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None |
Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).
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2024-01-16 |
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r-marqlevalg
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public |
This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.
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2024-01-16 |
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r-mapsf
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public |
Create and integrate thematic maps in your workflow. This package helps to design various cartographic representations such as proportional symbols, choropleth or typology maps. It also offers several functions to display layout elements that improve the graphic presentation of maps (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on 'base' graphics.
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2024-01-16 |
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r-mapdeck
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public |
Provides a mechanism to plot an interactive map using 'Mapbox GL' (<https://docs.mapbox.com/mapbox-gl-js/api/>), a javascript library for interactive maps, and 'Deck.gl' (<https://deck.gl/>), a javascript library which uses 'WebGL' for visualising large data sets.
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2024-01-16 |
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r-maptpx
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public |
Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis, as described in Taddy (2012) 'On estimation and selection for topic models'. Previous versions of this code were included as part of the 'textir' package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
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2024-01-16 |
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r-maptools
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None |
Please note that 'maptools' will be retired during October 2023, plan transition at your earliest convenience (see <https://r-spatial.org/r/2023/05/15/evolution4.html> and earlier blogs for guidance); some functionality will be moved to 'sp'. Set of tools for manipulating geographic data. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat.geom', 'maps', and others.
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2024-01-16 |
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r-mapdata
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public |
Supplement to maps package, providing some larger and/or higher-resolution databases. NOTE: this is a legacy package. The world map is out-dated.
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2024-01-16 |
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r-maps
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None |
Display of maps. Projection code and larger maps are in separate packages ('mapproj' and 'mapdata').
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2024-01-16 |
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r-mapproj
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None |
Converts latitude/longitude into projected coordinates.
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2024-01-16 |
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r-luminescence
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public |
A collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.
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2024-01-16 |
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r-mapfit
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public |
Estimation methods for phase-type distribution (PH) and Markovian arrival process (MAP) from empirical data (point and grouped data) and density function. The tool is based on the following researches: Okamura et al. (2009) <doi:10.1109/TNET.2008.2008750>, Okamura and Dohi (2009) <doi:10.1109/QEST.2009.28>, Okamura et al. (2011) <doi:10.1016/j.peva.2011.04.001>, Okamura et al. (2013) <doi:10.1002/asmb.1919>, Horvath and Okamura (2013) <doi:10.1007/978-3-642-40725-3_10>, Okamura and Dohi (2016) <doi:10.15807/jorsj.59.72>.
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2024-01-16 |
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r-manifoldoptim
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An R interface to version 0.3 of the 'ROPTLIB' optimization library (see <https://www.math.fsu.edu/~whuang2/> for more information). Optimize real- valued functions over manifolds such as Stiefel, Grassmann, and Symmetric Positive Definite matrices. For details see Martin et. al. (2020) <doi:10.18637/jss.v093.i01>. Note that the optional ldr package used in some of this package's examples can be obtained from either JSS <https://www.jstatsoft.org/index.php/jss/article/view/v061i03/2886> or from the CRAN archives <https://cran.r-project.org/src/contrib/Archive/ldr/ldr_1.3.3.tar.gz>.
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2024-01-16 |
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r-manlymix
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public |
The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models by Zhu and Melnykov (2016) <DOI:10.1016/j.csda.2016.01.015>. It also provides capabilities for forward and backward model selection procedures.
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2024-01-16 |
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r-mandelbrot
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public |
Estimates membership for the Mandelbrot set.
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2024-01-16 |
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r-maldiquant
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public |
A complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.
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2024-01-16 |
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r-mams
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public |
Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.
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2024-01-16 |
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r-magree
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public |
Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) <DOI:10.2307/2531148>. Implements Fortran 77 code for the methods developed by Schouten (1982) <DOI:10.1111/j.1467-9574.1982.tb00774.x>. Includes estimates of average agreement for each observer and average agreement for each subject.
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2024-01-16 |
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r-magrittr
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None |
Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions. For more information, see package vignette. To quote Rene Magritte, "Ceci n'est pas un pipe."
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2024-01-16 |
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r-lqmm
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Functions to fit quantile regression models for hierarchical data (2-level nested designs) as described in Geraci and Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>. A vignette is given in Geraci (2014, Journal of Statistical Software) <doi:10.18637/jss.v057.i13> and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses.
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2024-01-16 |
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r-mable
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Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>. For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.
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2024-01-16 |
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r-lutz
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Input latitude and longitude values or an 'sf/sfc' POINT object and get back the time zone in which they exist. Two methods are implemented. One is very fast and uses 'Rcpp' in conjunction with data from the 'Javascript' library (<https://github.com/darkskyapp/tz-lookup-oss/>). This method also works outside of countries' borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the 'sf' package to intersect points with a detailed map of time zones from here: <https://github.com/evansiroky/timezone-boundary-builder/>. The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect.
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2024-01-16 |
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r-lvmcomp
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Provides stochastic EM algorithms for latent variable models with a high-dimensional latent space. So far, we provide functions for confirmatory item factor analysis based on the multidimensional two parameter logistic (M2PL) model and the generalized multidimensional partial credit model. These functions scale well for problems with many latent traits (e.g., thirty or even more) and are virtually tuning-free. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale Full-information Item Factor Analysis. British Journal of Mathematical and Statistical Psychology. <doi:10.1111/bmsp.12153>.
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2024-01-16 |
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r-lpirfs
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Provides functions to estimate and visualize linear as well as nonlinear impulse responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>. The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.
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2024-01-16 |
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r-ltsa
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Methods of developing linear time series modelling. Methods are given for loglikelihood computation, forecasting and simulation.
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2024-01-16 |
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r-lsm
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public |
When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.
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2024-01-16 |
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r-lsei
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It contains functions that solve least squares linear regression problems under linear equality/inequality constraints. Functions for solving quadratic programming problems are also available, which transform such problems into least squares ones first. It is developed based on the 'Fortran' program of Lawson and Hanson (1974, 1995), which is public domain and available at <http://www.netlib.org/lawson-hanson/>.
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2024-01-16 |
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r-lpwc
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public |
Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) <doi:10.1186/s12859-019-3324-1>.
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2024-01-16 |
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r-lrcontrast
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Provides functions for calculating test statistics, simulating quantiles and simulating p-values of likelihood ratio contrast tests in regression models with a lack of identifiability.
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2024-01-16 |
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r-logistf
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public |
Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.
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2024-01-16 |
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r-lpsolveapi
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public |
The lpSolveAPI package provides an R interface to 'lp_solve', a Mixed Integer Linear Programming (MILP) solver with support for pure linear, (mixed) integer/binary, semi-continuous and special ordered sets (SOS) models.
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2024-01-16 |
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r-lpsolve
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public |
Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. In this implementation we supply a "wrapper" function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. This version calls lp_solve version 5.5.
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2024-01-16 |
