|
r-mblm
|
public |
Provides linear models based on Theil-Sen single median and Siegel repeated medians. They are very robust (29 or 50 percent breakdown point, respectively), and if no outliers are present, the estimators are very similar to OLS.
|
2024-01-16 |
|
r-mbir
|
public |
Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of 'mbir' is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.
|
2024-01-16 |
|
r-mbc
|
public |
Calibrate and apply multivariate bias correction algorithms for climate model simulations of multiple climate variables. Three methods described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented — (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr), and (iii) MBC N-dimensional PDF transform (MBCn) — as is the Rank Resampling for Distributions and Dependences (R2D2) method.
|
2024-01-16 |
|
r-mazeinda
|
public |
Methods for calculating and testing the significance of pairwise monotonic association from and based on the work of Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one or multiple sets can be performed in parallel thanks to the packages 'foreach' and 'doMC'.
|
2024-01-16 |
|
r-mazegen
|
public |
A maze generator that creates the Elithorn Maze (HTML file) and the functions to calculate the associated maze parameters (i.e. Difficulty and Ability).
|
2024-01-16 |
|
r-matlib
|
public |
A collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. These functions are mainly for tutorial purposes in learning matrix algebra ideas using R. In some cases, functions are provided for concepts available elsewhere in R, but where the function call or name is not obvious. In other cases, functions are provided to show or demonstrate an algorithm. In addition, a collection of functions are provided for drawing vector diagrams in 2D and 3D.
|
2024-01-16 |
|
r-maxskew
|
public |
It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on.
|
2024-01-16 |
|
r-maxnet
|
public |
Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options. See the 'Maxent' website <http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.
|
2024-01-16 |
|
r-maxmatching
|
public |
Computes the maximum matching for unweighted graph and maximum matching for (un)weighted bipartite graph efficiently.
|
2024-01-16 |
|
r-maxlike
|
public |
Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.
|
2024-01-16 |
|
r-maxlik
|
public |
Functions for Maximum Likelihood (ML) estimation, non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models.
|
2024-01-16 |
|
r-matskew
|
public |
Performs matrix skew-t parameter estimation, Gallaugher and McNicholas (2017) <doi: 10.1002/sta4.143>.
|
2024-01-16 |
|
r-matrixtests
|
public |
Functions to perform fast statistical hypothesis tests on rows/columns of matrices. The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use, 3) compatibility with tests implemented in R (like those available in the 'stats' package).
|
2024-01-16 |
|
r-matrixstructest
|
public |
Tests for block-diagonal structure in symmetric matrices (e.g. correlation matrices) under the null hypothesis of exchangeable off-diagonal elements. As described in Segal et al. (2019), these tests can be useful for construct validation either by themselves or as a complement to confirmatory factor analysis. Monte Carlo methods are used to approximate the permutation p-value with Hubert's Gamma (Hubert, 1976) and a t-statistic. This package also implements the chi-squared statistic described by Steiger (1980). Please see Segal, et al. (2019) <doi:10.1007/s11336-018-9647-4> for more information.
|
2024-01-16 |
|
r-matlab2r
|
public |
Allows users familiar with MATLAB to use MATLAB-named functions in R. Several basic MATLAB functions are written in this package to mimic the behavior of their original counterparts, with more to come as this package grows.
|
2024-01-16 |
|
r-matrixmodels
|
public |
Modelling with sparse and dense 'Matrix' matrices, using modular prediction and response module classes.
|
2024-01-16 |
|
r-matrixnormal
|
public |
Computes densities, probabilities, and random deviates of the Matrix Normal (Pocuca et al. (2019) <doi:10.48550/arXiv.1910.02859>). Also includes simple but useful matrix functions. See the vignette for more information.
|
2024-01-16 |
|
r-matrixeqtl
|
public |
Matrix eQTL is designed for fast eQTL analysis on large datasets. Matrix eQTL can test for association between genotype and gene expression using linear regression with either additive or ANOVA genotype effects. The models can include covariates to account for factors as population stratification, gender, and clinical variables. It also supports models with heteroscedastic and/or correlated errors, false discovery rate estimation and separate treatment of local (cis) and distant (trans) eQTLs. For more details see Shabalin (2012) <doi:10.1093/bioinformatics/bts163>.
|
2024-01-16 |
|
r-marss
|
public |
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>.
|
2024-01-16 |
|
r-matrixcalc
|
public |
A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563.
|
2024-01-16 |
|
r-matpow
|
public |
A general framework for computing powers of matrices. A key feature is the capability for users to write callback functions, called after each iteration, thus enabling customization for specific applications. Diverse types of matrix classes/matrix multiplication are accommodated. If the multiplication type computes in parallel, then the package computation is also parallel.
|
2024-01-16 |
|
r-matchthem
|
public |
Provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for more details).
|
2024-01-16 |
|
r-matlabr
|
public |
Provides users to call MATLAB from using the "system" command. Allows users to submit lines of code or MATLAB m files. This is in comparison to 'R.matlab', which creates a MATLAB server.
|
2024-01-16 |
|
r-marmap
|
public |
Import xyz data from the NOAA (National Oceanic and Atmospheric Administration, <https://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans, <https://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
|
2024-01-16 |
|
r-matlab
|
public |
Emulate 'MATLAB' code using 'R'.
|
2024-01-16 |
|
r-matconv
|
public |
Transferring over a code base from Matlab to R is often a repetitive and inefficient use of time. This package provides a translator for Matlab / Octave code into R code. It does some syntax changes, but most of the heavy lifting is in the function changes since the languages are so similar. Options for different data structures and the functions that can be changed are given. The Matlab code should be mostly in adherence to the standard style guide but some effort has been made to accommodate different number of spaces and other small syntax issues. This will not make the code more R friendly and may not even run afterwards. However, the rudimentary syntax, base function and data structure conversion is done quickly so that the maintainer can focus on changes to the design structure.
|
2024-01-16 |
|
r-mata
|
public |
Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence intervals, and MATA-Wald confidence densities and distributions, which are constructed using single-model frequentist estimators and model weights. See Turek and Fletcher (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019) <doi:10.1007/s10651-019-00432-5> for details.
|
2024-01-16 |
|
r-masstimate
|
public |
Estimation equations are from a variety of sources and associated error estimation.
|
2024-01-16 |
|
r-massign
|
public |
Constructing matrices for quick prototyping can be a nuisance, requiring the user to think about how to fill the matrix with values using the matrix() function. The %<-% operator solves that issue by allowing the user to construct matrices using code that shows the actual matrices.
|
2024-01-16 |
|
r-masae
|
public |
An S4 implementation of the unbiased extension of the model- assisted synthetic-regression estimator proposed by Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013) <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014) <DOI:10.1139/cjfr-2013-0449>. It yields smaller variances than the standard bias correction, the generalised regression estimator.
|
2024-01-16 |
|
r-marketmatching
|
public |
For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the 'dtw' package) to do the matching and the 'CausalImpact' package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).
|
2024-01-16 |
|
r-mapview
|
public |
Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.
|
2024-01-16 |
|
r-markmyassignment
|
public |
Automatic marking of R assignments for students and teachers based on 'testthat' test suites.
|
2024-01-16 |
|
r-margins
|
public |
An R port of Stata's 'margins' command, which can be used to calculate marginal (or partial) effects from model objects.
|
2024-01-16 |
|
r-markdown
|
None |
Render Markdown to full and lightweight HTML/LaTeX documents with the 'commonmark' package. It also supports features that are missing in 'commonmark', such as raw HTML/LaTeX blocks, LaTeX math, superscripts, subscripts, footnotes, element attributes, appendices, and fenced 'Divs'. With additional JavaScript and CSS, it can also create HTML slides and articles.
|
2024-01-16 |
|
r-marima
|
public |
Multivariate ARIMA and ARIMA-X estimation using Spliid's algorithm (marima()) and simulation (marima.sim()).
|
2024-01-16 |
|
r-maptiles
|
public |
To create maps from tiles, 'maptiles' downloads, composes and displays tiles from a large number of providers (e.g. 'OpenStreetMap', 'Stamen', 'Esri', 'CARTO', or 'Thunderforest').
|
2024-01-16 |
|
r-mareymap
|
public |
Local recombination rates are graphically estimated across a genome using Marey maps.
|
2024-01-16 |
|
r-march
|
public |
Computation of various Markovian models for categorical data including homogeneous Markov chains of any order, MTD models, Hidden Markov models, and Double Chain Markov Models.
|
2024-01-16 |
|
r-mar
|
public |
R functions for the estimation and eigen-decomposition of multivariate autoregressive models.
|
2024-01-16 |
|
r-mapedit
|
public |
Suite of interactive functions and helpers for selecting and editing geospatial data.
|
2024-01-16 |
|
r-maptree
|
public |
Functions with example data for graphing, pruning, and mapping models from hierarchical clustering, and classification and regression trees.
|
2024-01-16 |
|
r-mapiso
|
public |
Regularly spaced grids containing continuous data are transformed to contour polygons. A grid can be defined by a data.frame (x, y, value), an 'sf' object or a raster from 'terra'.
|
2024-01-16 |
|
r-mapa
|
public |
Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.
|
2024-01-16 |
|
r-mapplots
|
public |
Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.
|
2024-01-16 |
|
r-mapmisc
|
public |
Provides a minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections. See Brown (2016) <doi:10.32614/RJ-2016-005>.
|
2024-01-16 |
|
r-manhattanly
|
public |
Create interactive manhattan, Q-Q and volcano plots that are usable from the R console, in 'Dash' apps, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover the mouse pointer over a point to show details or drag a rectangle to zoom. A manhattan plot is a popular graphical method for visualizing results from high-dimensional data analysis such as a (epi)genome wide association study (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter plot against their genomic position. Manhattan plots are used for visualizing potential regions of interest in the genome that are associated with a phenotype. Interactive manhattan plots allow the inspection of specific value (e.g. rs number or gene name) by hovering the mouse over a cell, as well as zooming into a region of the genome (e.g. a chromosome) by dragging a rectangle around the relevant area. This work is based on the 'qqman' package and the 'plotly.js' engine. It produces similar manhattan and Q-Q plots as the 'manhattan' and 'qq' functions in the 'qqman' package, with the advantage of including extra annotation information and interactive web-based visualizations directly from R. Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) can be viewed and modified in a web browser.
|
2024-01-16 |
|
r-manynet
|
public |
A set of tools for making, manipulating, and mapping many different types of networks. All functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, and on one-mode, two-mode (bipartite), and sometimes three-mode networks. The package includes functions for importing and exporting, creating and generating networks, molding and manipulating networks and node and tie attributes, and describing and visualizing networks with sensible defaults.
|
2024-01-16 |
|
r-maldiquantforeign
|
public |
Functions for reading (tab, csv, Bruker fid, Ciphergen XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and writing (tab, csv, mMass MSD, mzML, imzML) different file formats of mass spectrometry data into/from 'MALDIquant' objects.
|
2024-01-16 |
|
r-manytests
|
public |
Performs the multiple testing procedures of Cox (2011) <doi:10.5170/CERN-2011-006> and Wong and Cox (2007) <doi:10.1080/02664760701240014>.
|
2024-01-16 |
