This package provides a set of wrapper functions that mainly re-produces most of the sequence plots rendered with TraMineR::seqplot(). Whereas TraMineR uses base R to produce the plots this library draws on ggplot2'. The plots are produced on the basis of a sequence object defined with TraMineR::seqdef(). The package automates the reshaping and plotting of sequence data. Resulting plots are of class ggplot', i.e. components can be added and tweaked using + and regular ggplot2 functions.

Graphical tools and goodness-of-fit tests for right-censored data: 1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests, which use the empirical distribution function for complete data and are extended for right-censored data. 2. Generalized chi-squared-type test, which is based on the squared differences between observed and expected counts using random cells with right-censored data. 3. A series of graphical tools such as probability or cumulative hazard plots to guide the decision about the most suitable parametric model for the data. These functions share several features as they can handle both complete and right-censored data, and they provide parameter estimates for the distributions under study.

This package provides functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02> for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy.

This package implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2023) <doi:10.1007/s00190-023-01702-8>. The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals. The gmwmx package provides functions to estimate, compare and analyze models, utilities to load and work with Global Navigation Satellite System (GNSS) data as well as methods to compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector.

This package contains methods for fitting Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). Generalized regression models are common methods for handling data for which assuming Gaussian-distributed errors is not appropriate. For instance, if the response of interest is binary, count, or proportion data, one can instead model the expectation of the response based on an appropriate data-generating distribution. This package provides methods for fitting GLMs and GAMs under Beta regression, Poisson regression, Gamma regression, and Binomial regression (currently GLM only) settings. Models are fit using local scoring algorithms described in Hastie and Tibshirani (1990) <doi:10.1214/ss/1177013604>.

Interact with the Google Cloud Vision <https://cloud.google.com/vision/> API in R. Part of the cloudyr <https://cloudyr.github.io/> project.

Datos de nombres inscritos en Chile entre 1920 y 2021, de acuerdo al Servicio de Registro Civil. English: Chilean baby names registered from 1920 to 2021 by the Civil Registry Service.

This package implements the five-parameter Generalized Kumaraswamy ('gkw') distribution proposed by Carrasco, Ferrari and Cordeiro (2010) <doi:10.48550/arXiv.1004.0911> and its seven nested sub-families for modeling bounded continuous data on the unit interval (0,1). The gkw distribution extends the Kumaraswamy distribution described by Jones (2009) <doi:10.1016/j.stamet.2008.04.001>. Provides density, distribution, quantile, and random generation functions, along with analytical log-likelihood, gradient, and Hessian functions implemented in C++ via RcppArmadillo for maximum computational efficiency. Suitable for modeling proportions, rates, percentages, and indices exhibiting complex features such as asymmetry, or heavy tails and other shapes not adequately captured by standard distributions like simple Beta or Kumaraswamy.

Collection of functions to enhance ggplot2 and ggiraph'. Provides functions for exploratory plots. All plot can be a static plot or an interactive plot using ggiraph'.

Discretize multivariate continuous data using a grid to capture the joint distribution that preserves clusters in original data. It can handle both labeled or unlabeled data. Both published methods (Wang et al 2020) <doi:10.1145/3388440.3412415> and new methods are included. Joint grid discretization can prepare data for model-free inference of association, function, or causality.

This package provides a suite of custom R Markdown formats and templates for authoring web pages styled with the GOV.UK Design System.

This is a companion to Henry-Stewart talk by Zhao (2026, <doi:10.69645/FRFQ9519>), which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. The earlier version by Zhao (2009, <doi:10.69645/DCRY5578>) provides an brief introduction to these topics.

This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms.

Association analysis between categorical variables using the Goodman and Kruskal tau measure. This asymmetric association measure allows the detection of asymmetric relations between categorical variables (e.g., one variable obtained by re-grouping another).

Group SLOPE (Group Sorted L1 Penalized Estimation) is a penalized linear regression method that is used for adaptive selection of groups of significant predictors in a high-dimensional linear model. The Group SLOPE method can control the (group) false discovery rate at a user-specified level (i.e., control the expected proportion of irrelevant among all selected groups of predictors). For additional information about the implemented methods please see Brzyski, Gossmann, Su, Bogdan (2018) <doi:10.1080/01621459.2017.1411269>.

This package implements GINA-X, a genome-wide iterative fine-mapping method designed for non-Gaussian traits. It supports the identification of credible sets of genetic variants.

Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.

This package provides a ggplot2 extension that enables visualization of IP (Internet Protocol) addresses and networks. The address space is mapped onto the Cartesian coordinate system using a space-filling curve. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces.

Uses ggplot2 to create normally distributed violin plots with specified means and standard deviations. This function can be useful in showing hypothetically normal distributions and confidence intervals.

An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).

Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.

R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.

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.

This package provides a Bayesian model selection approach for generalized linear mixed models. Currently, GLMMselect can be used for Poisson GLMM and Bernoulli GLMM. GLMMselect can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. GLMMselect can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. GLMMselect is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.