This package creates ideal data for all distributions in the generalized linear model framework.

Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.

An implementation of functions to display Greek letters on the RStudio (include subscript and superscript indexes) and RGui (without subscripts and only with superscript 1, 2 or 3; because RGui doesn't support printing the corresponding Unicode characters as a string: all subscripts ranging from 0 to 9 and superscripts equal to 0, 4, 5, 6, 7, 8 or 9). The functions in this package do not work properly on the R console. Characters are used via Unicode and encoded as UTF-8 to ensure that they can be viewed on all operating systems. Other characters related to mathematics are included, such as the infinity symbol. All this accessible from very simple commands. This is a package that can be used for teaching purposes, the statistical notation for hypothesis testing can be written from this package and so it is possible to build a course from the swirlify package. Another utility of this package is to create new summary functions that contain the functional form of the model adjusted with the Greek letters, thus making the transition from statistical theory to practice easier. In addition, it is a natural extension of the clisymbols package.

This package provides interactive visualisations for exploratory data analysis of high-dimensional datasets. Includes parallel coordinate plots for exploring large datasets with mostly quantitative features, but also stacked one-dimensional visualisations that more effectively show missingness and complex categorical relationships in smaller datasets.

Decomposes observed growth in agricultural and livestock systems into interpretable component effects. Depending on the application, the total change in output can be attributed to components such as area effect, yield effect, herd or slaughter effect, productivity effect, and interaction effect. Details can be found in Rakshit and Bardhan (2026) <doi:10.1007/s11250-026-04988-w>.

Supply implementation to model generalized multivariate functional data using Bayesian additive mixed models of R package bamlss via a latent Gaussian process (see Umlauf, Klein, Zeileis (2018) <doi:10.1080/10618600.2017.1407325>).

This package provides functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.

Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.

This package provides tools to compute the Generalized Measure of Correlation (GMC), a dependence measure accounting for nonlinearity and asymmetry in the relationship between variables. Based on the method proposed by Zheng, Shi, and Zhang (2012) <doi:10.1080/01621459.2012.710509>.

This package provides ggsnap(), which saves a ggplot2 plot to disk in a + chain, acting as a thin, chainable wrapper around ggplot2::ggsave(). Can be called multiple times in a chain to save different snapshots.

Computes the probability density, survival function, the hazard rate functions and generates random samples from the GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. The likelihood estimates, the randomized quantile (Louzada, F., et al. (2020) <doi:10.1109/ACCESS.2020.3040525>) residuals and the normally transformed randomized survival probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>) residuals are obtained for the GTDL model.

This package provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day ('GSOD') weather data from the from the USA National Centers for Environmental Information ('NCEI'). The data were retired on 2025-08-29 and are no longer updated. Units are converted from from United States Customary System ('USCS') units to International System of Units ('SI'). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative humidity ('RH') are calculated from the original data using the improved August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags. For information on the GSOD data from NCEI', please see the GSOD readme.txt file available from, <https://www.ncei.noaa.gov/pub/data/gsod/readme.txt>.

Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.

Read examples with interlinear glosses from files or from text and print them in a way compatible with both Latex and HTML outputs.

Functional denoising and functional ANOVA through wavelet-domain Markov groves. Fore more details see: Ma L. and Soriano J. (2018) Efficient functional ANOVA through wavelet-domain Markov groves. <arXiv:1602.03990v2 [stat.ME]>.

The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes rule.

Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.

Enhance a mice imputation workflow with visualizations for incomplete and/or imputed data. The plotting functions produce ggplot objects which may be easily manipulated or extended. Use ggmice to inspect missing data, develop imputation models, evaluate algorithmic convergence, or compare observed versus imputed data.

This package provides a nonparametric empirical Bayes method for recovering gradients (or growth velocities) from observations of smooth functions (e.g., growth curves) at isolated time points.

This package provides an interface to the system-level grep utility for efficiently reading, filtering, and aggregating data from multiple flat files. By pre-filtering data at the command line before it enters the R environment, the package reduces memory overhead and improves ingestion speed. Includes functions for counting records across large file systems and supports recursive directory searching.

Implementations of the algorithms present article Generalized Spatial-Time Sequence Miner, original title (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD - Simpósio Brasileiro de Banco de Dados, 2021 <doi:10.5753/sbbd.2021.17891>).

It is well known that the distribution of a Gaussian ratio does not follow a Gaussian distribution. The lack of awareness among users of vegetation indices about this non-Gaussian nature could lead to incorrect statistical modeling and interpretation. This package provides tools to accurately handle and analyse such ratios: density function, parameter estimation, simulation. An example on the study of chlorophyll fluorescence can be found in A. El Ghaziri et al. (2023) <doi:10.3390/rs15020528> and another method for parameter estimation is given in Bouhlel et al. (2023) <doi:10.23919/EUSIPCO58844.2023.10290111>.

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 icon geometries for ggplot2', using vector icon sets from the icons package. Icons can be drawn as points in place of ordinary markers, styled with the usual colour, size, alpha and angle aesthetics, and mapped from discrete values or passed through directly. Icons also appear in legend keys, as fixed-position annotations, and as axis, strip and legend labels. Pictograms extend this to isotype-style unit charts, waffle/percentage charts and rating widgets, encoding a value as a grid of repeated icons.