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r-margins
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An R port of Stata's 'margins' command, which can be used to calculate marginal (or partial) effects from model objects.
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2025-04-22 |
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r-maptiles
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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').
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2025-04-22 |
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r-mapspain
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Administrative Boundaries of Spain at several levels (Autonomous Communities, Provinces, Municipalities) based on the 'GISCO' 'Eurostat' database <https://ec.europa.eu/eurostat/web/gisco> and 'CartoBase SIANE' from 'Instituto Geografico Nacional' <https://www.ign.es/>. It also provides a 'leaflet' plugin and the ability of downloading and processing static tiles.
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2025-04-22 |
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r-mapedit
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Suite of interactive functions and helpers for selecting and editing geospatial data.
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2025-04-22 |
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r-mapiso
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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'.
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2025-04-22 |
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r-mapa
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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>.
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2025-04-22 |
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r-manhattanly
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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.
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2025-04-22 |
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r-manynet
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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.
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2025-04-22 |
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r-maldiquantforeign
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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.
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2025-04-22 |
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r-manova.rm
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Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.
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2025-04-22 |
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r-magicaxis
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Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.
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2025-04-22 |
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r-lvplot
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Implements the letter value 'boxplot' which extends the standard 'boxplot' to deal with both larger and smaller number of data points by dynamically selecting the appropriate number of letter values to display.
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2025-04-22 |
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r-luz
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A high level interface for 'torch' providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to support expressing a large range of models. It's heavily inspired by 'fastai' by Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.
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2025-04-22 |
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r-ltm
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Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
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2025-04-22 |
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r-lpdensity
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Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.
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2025-04-22 |
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r-lookup
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Simple functions to lookup items in key-value pairs. See Mehta (2021) <doi:10.1007/978-1-4842-6613-7_6>.
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2025-04-22 |
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r-logr
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Contains functions to help create log files. The package aims to overcome the difficulty of the base R sink() command. The log_print() function will print to both the console and the file log, without interfering in other write operations.
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2025-04-22 |
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r-logmult
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Functions to fit log-multiplicative models using 'gnm', with support for convenient printing, plots, and jackknife/bootstrap standard errors. For complex survey data, models can be fitted from design objects from the 'survey' package. Currently supported models include UNIDIFF (Erikson & Goldthorpe, 1992), a.k.a. log-multiplicative layer effect model (Xie, 1992) <doi:10.2307/2096242>, and several association models: Goodman (1979) <doi:10.2307/2286971> row-column association models of the RC(M) and RC(M)-L families with one or several dimensions; two skew-symmetric association models proposed by Yamaguchi (1990) <doi:10.2307/271086> and by van der Heijden & Mooijaart (1995) <doi:10.1177/0049124195024001002> Functions allow computing the intrinsic association coefficient (see Bouchet-Valat (2022) <doi:10.1177/0049124119852389>) and the Altham (1970) index <doi:10.1111/j.2517-6161.1970.tb00816.x>, including via the Bayes shrinkage estimator proposed by Zhou (2015) <doi:10.1177/0081175015570097>; and the RAS/IPF/Deming-Stephan algorithm.
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2025-04-22 |
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r-logitr
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Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.
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2025-04-22 |
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r-loggit
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An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary log-writing interfaces. It provides a set of wrappings for base R's message(), warning(), and stop() functions that maintain identical functionality, but also log the handler message to an 'ndjson' log file. 'loggit' also exports its internal 'loggit()' function for powerful and configurable custom logging. No change in existing code is necessary to use this package, and should only require additions to fully leverage the power of the logging system. 'loggit' also provides a log reader for reading an 'ndjson' log file into a data frame, log rotation, and live echo of the 'ndjson' log messages to terminal 'stdout' for log capture by external systems (like containers). 'loggit' is ideal for Shiny apps, data pipelines, modeling work flows, and more. Please see the vignettes for detailed example use cases.
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2025-04-22 |
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r-logcondens
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Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
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2025-04-22 |
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r-locatexec
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A set of functions to locate some programs available on the user machine. The package provides functions to locate 'Node.js', 'npm', 'LibreOffice', 'Microsoft Word', 'Microsoft PowerPoint', 'Microsoft Excel', 'Python', 'pip', 'Mozilla Firefox' and 'Google Chrome'. User can test the availability of a program with eventually a version and call it with function system2() or system(). This allows the use of a single function to retrieve the path to a program regardless of the operating system and its configuration.
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2025-04-22 |
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r-lmomco
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Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L].
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2025-04-22 |
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r-loadings
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Computing statistical hypothesis testing for loading in principal component analysis (PCA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>), orthogonal smoothed PCA (OS-PCA) (Yamamoto, H. et al. (2021) <doi:10.3390/metabo11030149>), one-sided kernel PCA (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>), partial least squares (PLS) and PLS discriminant analysis (PLS-DA) (Yamamoto, H. et al. (2009) <doi:10.1016/j.chemolab.2009.05.006>), PLS with rank order of groups (PLS-ROG) (Yamamoto, H. (2017) <doi:10.1002/cem.2883>), regularized canonical correlation analysis discriminant analysis (RCCA-DA) (Yamamoto, H. et al. (2008) <doi:10.1016/j.bej.2007.12.009>), multiset PLS and PLS-ROG (Yamamoto, H. (2022) <doi:10.1101/2022.08.30.505949>).
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2025-04-22 |
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r-loa
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Various plots and functions that make use of the lattice/trellis plotting framework. The plots, which include loaPlot(), RgoogleMapsPlot() and trianglePlot(), use panelPal(), a function that extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel and panel-to-key synchronization/management.
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2025-04-22 |
