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r-glcm
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Enables calculation of image textures (Haralick 1973) <doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices (GLCMs). Supports processing images that cannot fit in memory.
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2025-03-25 |
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r-glassofast
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A fast and improved implementation of the graphical LASSO.
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2025-03-25 |
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r-glasso
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Estimation of a sparse inverse covariance matrix using a lasso (L1) penalty. Facilities are provided for estimates along a path of values for the regularization parameter.
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2025-03-25 |
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r-glamlasso
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Functions capable of performing efficient design matrix free penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The generic glamlasso() function solves the penalized maximum likelihood estimation (PMLE) problem in a pure generalized linear array model (GLAM) as well as in a GLAM containing a non-tensor component. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the followings models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. Furthermore this package also contains two functions that can be used to fit special cases of GLAMs, see glamlassoRR() and glamlassoS(). The procedure underlying these functions is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>.
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2025-03-25 |
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r-giraf
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Allows calculation on, and sampling from Gibbs Random Fields, and more precisely general homogeneous Potts model. The primary tool is the exact computation of the intractable normalising constant for small rectangular lattices. Beside the latter function, it contains method that give exact sample from the likelihood for small enough rectangular lattices or approximate sample from the likelihood using MCMC samplers for large lattices.
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2025-03-25 |
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r-gigrvg
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Generator and density function for the Generalized Inverse Gaussian (GIG) distribution.
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2025-03-25 |
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r-gifi
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Implements categorical principal component analysis ('PRINCALS'), multiple correspondence analysis ('HOMALS'), monotone regression analysis ('MORALS'). It replaces the 'homals' package.
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2025-03-25 |
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r-ghyp
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Detailed functionality for working with the univariate and multivariate Generalized Hyperbolic distribution and its special cases (Hyperbolic (hyp), Normal Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t and Gaussian distribution). Especially, it contains fitting procedures, an AIC-based model selection routine, and functions for the computation of density, quantile, probability, random variates, expected shortfall and some portfolio optimization and plotting routines as well as the likelihood ratio test. In addition, it contains the Generalized Inverse Gaussian distribution.
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2025-03-25 |
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r-ggmselect
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Graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.
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2025-03-25 |
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r-gglasso
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A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.
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2025-03-25 |
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r-gforce
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A complete suite of computationally efficient methods for high dimensional clustering and inference problems in G-Latent Models (a type of Latent Variable Gaussian graphical model). The main feature is the FORCE (First-Order, Certifiable, Efficient) clustering algorithm which is a fast solver for a semi-definite programming (SDP) relaxation of the K-means problem. For certain types of graphical models (G-Latent Models), with high probability the algorithm not only finds the optimal clustering, but produces a certificate of having done so. This certificate, however, is model independent and so can also be used to certify data clustering problems. The 'GFORCE' package also contains implementations of inferential procedures for G-Latent graphical models using n-fold cross validation. Also included are native code implementations of other popular clustering methods such as Lloyd's algorithm with kmeans++ initialization and complete linkage hierarchical clustering. The FORCE method is due to Eisenach and Liu (2019) <arxiv:1806.00530>.
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2025-03-25 |
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r-gettz
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A function to retrieve the system timezone on Unix systems which has been found to find an answer when 'Sys.timezone()' has failed. It is based on an answer by Duane McCully posted on 'StackOverflow', and adapted to be callable from R. The package also builds on Windows, but just returns NULL.
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2025-03-25 |
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r-getpass
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A micro-package for reading "passwords", i.e. reading user input with masking, so that the input is not displayed as it is typed. Currently we have support for 'RStudio', the command line (every OS), and any platform where 'tcltk' is present.
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2025-03-25 |
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r-geostatsp
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Geostatistical modelling facilities using Raster and SpatialPoints objects are provided. Non-Gaussian models are fit using INLA, and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>.
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2025-03-25 |
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r-geosphere
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Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
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2025-03-25 |
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r-geoops
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Tools for doing calculations and manipulations on 'GeoJSON', a 'geospatial' data interchange format (<https://tools.ietf.org/html/rfc7946>). 'GeoJSON' is also valid 'JSON'.
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2025-03-25 |
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r-geojsonr
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Includes functions for processing GeoJson objects <https://en.wikipedia.org/wiki/GeoJSON> relying on 'RFC 7946' <https://tools.ietf.org/pdf/rfc7946.pdf>. The geojson encoding is based on 'json11', a tiny JSON library for 'C++11' <https://github.com/dropbox/json11>. Furthermore, the source code is exported in R through the 'Rcpp' and 'RcppArmadillo' packages.
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2025-03-25 |
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r-geohashtools
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Tools for working with Gustavo Niemeyer's geohash coordinate system, ported to R from Hiroaki Kawai's 'Python' implementation and embellished to sit naturally in the R ecosystem.
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2025-03-25 |
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r-geodist
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Dependency-free, ultra fast calculation of geodesic distances. Includes the reference nanometre-accuracy geodesic distances of Karney (2013) <doi:10.1007/s00190-012-0578-z>, as used by the 'sf' package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances.
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2025-03-25 |
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r-geocount
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This package provides a variety of functions to analyze and model geostatistical count data with generalized linear spatial models, including 1) simulate and visualize the data; 2) posterior sampling with robust MCMC algorithms (in serial or parallel way); 3) perform prediction for unsampled locations; 4) conduct Bayesian model checking procedure to evaluate the goodness of fitting; 5) conduct transformed residual checking procedure. In the package, seamlessly embedded C++ programs and parallel computing techniques are implemented to speed up the computing processes.
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2025-03-25 |
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r-gensvm
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The GenSVM classifier is a generalized multiclass support vector machine (SVM). This classifier aims to find decision boundaries that separate the classes with as wide a margin as possible. In GenSVM, the loss function is very flexible in the way that misclassifications are penalized. This allows the user to tune the classifier to the dataset at hand and potentially obtain higher classification accuracy than alternative multiclass SVMs. Moreover, this flexibility means that GenSVM has a number of other multiclass SVMs as special cases. One of the other advantages of GenSVM is that it is trained in the primal space, allowing the use of warm starts during optimization. This means that for common tasks such as cross validation or repeated model fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den Burg and P.J.F. Groenen (2018) <http://www.jmlr.org/papers/v17/14-526.html>.
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2025-03-25 |
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r-gensurv
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Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model.
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2025-03-25 |
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r-gensa
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Performs search for global minimum of a very complex non-linear objective function with a very large number of optima.
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2025-03-25 |
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r-genodds
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Calculates Agresti's (1980) <https://www.jstor.org/stable/2530495> generalized odds ratios. For a randomly selected pair of observations from two groups, calculates the odds that the second group will have a higher scoring outcome than that of the first group. Package provides hypothesis testing for if this odds ratio is significantly different to 1 (equal chance).
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2025-03-25 |
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r-genlasso
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Provides fast algorithms for computing the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two problems are given to improve stability and speed.
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2025-03-25 |
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r-genkern
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Computes generalised KDEs
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2025-03-25 |
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r-genie
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A new hierarchical clustering linkage criterion: the Genie algorithm links two clusters in such a way that a chosen economic inequity measure (e.g., the Gini index) of the cluster sizes does not increase drastically above a given threshold. Benchmarks indicate a high practical usefulness of the introduced method: it most often outperforms the Ward or average linkage in terms of the clustering quality while retaining the single linkage speed, see (Gagolewski et al. 2016a <DOI:10.1016/j.ins.2016.05.003>, 2016b <DOI:10.1007/978-3-319-45656-0_16>) for more details.
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2025-03-25 |
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r-genepop
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Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
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2025-03-25 |
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r-genepi
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Functions for Genetic Epi Methods Developed at MSKCC
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2025-03-25 |
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r-gelnet
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Implements several extensions of the elastic net regularization scheme. These extensions include individual feature penalties for the L1 term, feature-feature penalties for the L2 term, as well as translation coefficients for the latter.
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2025-03-25 |
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r-geigen
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Functions to compute generalized eigenvalues and eigenvectors, the generalized Schur decomposition and the generalized Singular Value Decomposition of a matrix pair, using Lapack routines.
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2025-03-25 |
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r-geepack
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Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses.
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2025-03-25 |
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r-gee
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Generalized Estimation Equation solver.
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2025-03-25 |
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r-gee4
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Fit joint mean-covariance models for longitudinal data within the framework of (weighted) generalised estimating equations (GEE/WGEE). The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Armadillo' C++ library for numerical linear algebra and 'RcppArmadillo' glue.
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2025-03-25 |
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r-gdpc
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Functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>.
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2025-03-25 |
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r-gdmp
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Manage and analyze high-dimensional SNP data from chips with multiple densities.
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2025-03-25 |
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r-gcpm
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Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities.
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2025-03-25 |
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r-gconcord
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Estimates a sparse inverse covariance matrix from a convex pseudo-likelihood function with L1 penalty
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2025-03-25 |
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r-gckrig
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Provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations.
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2025-03-25 |
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r-gcdnet
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A generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the LASSO and elastic net (adaptive) penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.
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2025-03-25 |
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r-gcat
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These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories.
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2025-03-25 |
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r-gbm
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An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.
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2025-03-25 |
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r-gb
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A collection of algorithms and functions for fitting data to a generalized lambda distribution via moment matching methods, and generalized bootstrapping.
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2025-03-25 |
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r-gausscov
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Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets. Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "A Model-free Approach to Linear Least Squares Regression with Exact Probabilities and Applications to Covariate Selection", 2019, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633v4>.
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2025-03-25 |
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r-gaselect
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Provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.
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2025-03-25 |
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r-gap
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It is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates.
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2025-03-25 |
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r-gammslice
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Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.
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2025-03-25 |
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r-gamlss.dist
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A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a ''log'' or a ''logit' transformation respectively.
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2025-03-25 |
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r-gamlr
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The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2017 JCGS), One-Step Estimator Paths for Concave Regularization, <arXiv:1308.5623>.
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2025-03-25 |
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r-gamesga
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Finds adaptive strategies for sequential symmetric games using a genetic algorithm. Currently, any symmetric two by two matrix is allowed, and strategies can remember the history of an opponent's play from the previous three rounds of moves in iterated interactions between players. The genetic algorithm returns a list of adaptive strategies given payoffs, and the mean fitness of strategies in each generation.
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2025-03-25 |
