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r-inext
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Provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation curves for species diversity (Hill numbers) based on individual-based abundance data or sampling-unit- based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution) for an introduction of the R package.
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2025-03-25 |
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r-inspectdf
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A collection of utilities for columnwise summary, comparison and visualisation of data frames. Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage.
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2025-03-25 |
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r-inlaspacetime
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Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (<https://www.r-inla.org>). These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal models in Lindgren et. al. (2023) <arXiv:2006.04917>. Details are provided in the available vignettes and from the URL bellow.
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2025-03-25 |
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r-immunarch
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A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq) and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods, such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor databases and clonotype tracking in vaccination and cancer studies. A successor to our previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.
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2025-03-25 |
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r-imbalance
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Class imbalance usually damages the performance of classifiers. Thus, it is important to treat data before applying a classifier algorithm. This package includes recent resampling algorithms in the literature: (Barua et al. 2014) <doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>, (Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014) <doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014) <doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to perform oversampling.
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2025-03-25 |
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r-ichimoku
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An implementation of 'Ichimoku Kinko Hyo', also commonly known as 'cloud charts'. Static and interactive visualizations with tools for creating, backtesting and development of quantitative 'ichimoku' strategies. As described in Sasaki (1996, ISBN:4925152009), the technique is a refinement on candlestick charting, originating from Japan and now in widespread use in technical analysis worldwide. Translating as 'one-glance equilibrium chart', it allows the price action and market structure of financial securities to be determined 'at-a-glance'. Incorporates an interface with the OANDA fxTrade API <https://developer.oanda.com/> for retrieving historical and live streaming price data for major currencies, metals, commodities, government bonds and stock indices.
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2025-03-25 |
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r-hypervolume
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Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
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2025-03-25 |
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r-hutilscpp
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Provides utility functions that are simply, frequently used, but may require higher performance that what can be obtained from base R. Incidentally provides support for 'reverse geocoding', such as matching a point with its nearest neighbour in another array. Used as a complement to package 'hutils' by sacrificing compilation or installation time for higher running speeds. The name is a portmanteau of the author and 'Rcpp'.
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2025-03-25 |
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r-httpgd
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A graphics device for R that is accessible via network protocols. This package was created to make it easier to embed live R graphics in integrated development environments and other applications. The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics. The device asynchronously serves graphics via 'HTTP' and 'WebSockets'.
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2025-03-25 |
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r-hts
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Provides methods for analysing and forecasting hierarchical and grouped time series. The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al. 2011) <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.
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2025-03-25 |
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r-hlmdiag
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A suite of diagnostic tools for hierarchical (multilevel) linear models. The tools include not only leverage and traditional deletion diagnostics (Cook's distance, covratio, covtrace, and MDFFITS) but also convenience functions and graphics for residual analysis. Models can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package.
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2025-03-25 |
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r-hrqglas
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A program that conducts group variable selection for quantile and robust mean regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss. Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check function can be approximated by the Huber loss for the median and the tilted version of Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and has also been shown to be statistical consistent.
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2025-03-25 |
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r-hkprocess
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Methods to make inference about the Hurst-Kolmogorov (fractional Gaussian noise, fGn) and the AR(1) process. Related time series trend tests are also included.
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2025-03-25 |
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r-hitandrun
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The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) <doi:10.1016/j.ejor.2012.08.026>. van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) <doi:10.1016/j.ejor.2014.06.036>.
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2025-03-25 |
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r-hipread
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Read hierarchical fixed width files like those commonly used by many census data providers. Also allows for reading of data in chunks, and reading 'gzipped' files without storing the full file in memory.
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2025-03-25 |
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r-highs
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R interface to 'HiGHS', an optimization solver for solving mixed integer optimization problems with quadratic or linear objective and linear constraints.
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2025-03-25 |
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r-highfrequency
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Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity. A detailed vignette can be found in the open-access paper "Analyzing Intraday Financial Data in R: The highfrequency Package" by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>).
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2025-03-25 |
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r-hiclimr
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A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file. The technical details are described in Badr et al. (2015) <doi:10.1007/s12145-015-0221-7>.
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2025-03-25 |
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r-hclust1d
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Univariate agglomerative hierarchical clustering with a comprehensive list of choices of a linkage function in O(n*log n) time. The better algorithmic time complexity is paired with an efficient 'C++' implementation.
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2025-03-25 |
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r-hardyweinberg
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Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908; Weinberg, 1908) for bi and multi-allelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included (Graffelman & Weir, 2016) <doi:10.1038/hdy.2016.20>, including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots. The functionality of the package is explained in detail in a related JSS paper <doi:10.18637/jss.v064.i03>.
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2025-03-25 |
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r-harmony
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Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.
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2025-03-25 |
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r-haplo.stats
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Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.
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2025-03-25 |
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r-hal9001
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A scalable implementation of the highly adaptive lasso algorithm, including routines for constructing sparse matrices of basis functions of the observed data, as well as a custom implementation of Lasso regression tailored to enhance efficiency when the matrix of predictors is composed exclusively of indicator functions. For ease of use and increased flexibility, the Lasso fitting routines invoke code from the 'glmnet' package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) <doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>. This implementation of the highly adaptive lasso algorithm was described by Hejazi, Coyle, and van der Laan (2020) <doi:10.21105/joss.02526>.
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2025-03-25 |
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r-gwmodel
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Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.
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2025-03-25 |
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r-gunifrac
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A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.
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2025-03-25 |
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r-gtfstools
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Utility functions to read, manipulate, analyse and write transit feeds in the General Transit Feed Specification (GTFS) data format.
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2025-03-25 |
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r-gsynth
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Provides causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. This version supports unbalanced panels and implements the matrix completion method.
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2025-03-25 |
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r-gslnls
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An R interface to nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.
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2025-03-25 |
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r-gstat
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Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions; supports sf and stars.
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2025-03-25 |
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r-gsignal
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R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.
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2025-03-25 |
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r-gsdesign
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Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.
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2025-03-25 |
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r-gse
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Robust Estimation of Multivariate Location and Scatter in the Presence of Cellwise and Casewise Contamination and Missing Data.
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2025-03-25 |
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r-grim
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Provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models. Documentation about 'gRim' is provided by vignettes included in this package and the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>); see 'citation("gRim")' for details.
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2025-03-25 |
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r-greybox
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Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.
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2025-03-25 |
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r-grbase
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The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>) and in the paper by Dethlefsen and Højsgaard, (2005, <doi:10.18637/jss.v014.i17>). Please see 'citation("gRbase")' for citation details.
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2025-03-25 |
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r-graphicalvar
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Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
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2025-03-25 |
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r-grain
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Probability propagation in graphical independence networks, also known as Bayesian networks or probabilistic expert systems. Documentation of the package is provided in vignettes included in the package and in the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>). See 'citation("gRain")' for details.
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2025-03-25 |
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r-gpboost
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An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.
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2025-03-25 |
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r-gpareto
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Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.
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2025-03-25 |
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r-gnm
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Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others. Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.
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2025-03-25 |
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r-gmedian
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Fast algorithms for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.
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2025-03-25 |
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r-gmmat
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Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
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2025-03-25 |
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r-gmisc
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Tools for making the descriptive "Table 1" used in medical articles, a transition plot for showing changes between categories (also known as a Sankey diagram), flow charts by extending the grid package, a method for variable selection based on the SVD, Bézier lines with arrows complementing the ones in the 'grid' package, and more.
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2025-03-25 |
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r-gmcp
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Functions and a graphical user interface for graphical described multiple test procedures.
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2025-03-25 |
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r-glmmtmb
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Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.
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2025-03-25 |
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r-glmm
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Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
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2025-03-25 |
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r-gld
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The generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form. This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different types of the distribution, the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden and Loots 2009) and FM5 - see documentation for details. It provides the density function, distribution function, and Quantile-Quantile plots. It implements a variety of estimation methods for the distribution, including diagnostic plots. Estimation methods include the starship (all 4 types), method of L-Moments for the GPD and FKML types, and a number of methods for only the FKML type. These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, Trimmed L-Moments and Distributional Least Absolutes.
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2025-03-25 |
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r-ggwordcloud
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Provides a word cloud text geom for 'ggplot2'. Texts are placed so that they do not overlap as in 'ggrepel'. The algorithm used is a variation around the one of 'wordcloud2.js'.
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2025-03-25 |
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r-ggraph
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The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. ggraph is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer.
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2025-03-25 |
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r-ggpointdensity
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A cross between a 2D density plot and a scatter plot, implemented as a 'ggplot2' geom. Points in the scatter plot are colored by the number of neighboring points. This is useful to visualize the 2D-distribution of points in case of overplotting.
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2025-03-25 |
