Estimates the Gini index and computes variances and confidence intervals for finite and infinite populations, using different methods; also computes Gini index for continuous probability distributions, draws samples from continuous probability distributions with Gini indices set by the user; uses Rcpp'. References: Muñoz et al. (2023) <doi:10.1177/00491241231176847>. à lvarez et al. (2021) <doi:10.3390/math9243252>. Giorgi and Gigliarano (2017) <doi:10.1111/joes.12185>. Langel and Tillé (2013) <doi:10.1111/j.1467-985X.2012.01048.x>.

Add a scroll back to top Font Awesome icon <https://fontawesome.com/> in rmarkdown documents and shiny apps thanks to jQuery GoTop <https://scottdorman.blog/jquery-gotop/>.

This package provides functions that make it easy to reveal ggplot2 graphs incrementally. The functions take a plot produced with ggplot2 and return a list of plots showing data incrementally by panels, layers, groups, the values in an axis or any arbitrary aesthetic.

This package provides a comprehensive toolkit for scraping and analyzing book data from <https://www.goodreads.com/>. This package provides functions to search for books, scrape book details and reviews, perform sentiment analysis on reviews, and conduct topic modeling. It's designed for researchers, data analysts, and book enthusiasts who want to gain insights from Goodreads data.

The increasing popularity of geographically weighted (GW) techniques has resulted in the development of several R packages, such as GWmodel'. To facilitate their usages, GWmodelVis provides a shiny'-based interactive visualization toolkit for geographically weighted (GW) models. It includes a number of visualization tools, including dynamic mapping of parameter surfaces, statistical visualization, sonification and exporting videos via FFmpeg'.

The standard linear regression theory whether frequentist or Bayesian is based on an assumed (revealed?) truth (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper "An Approximation Based Theory of Linear Regression". Only four simple equations are required. Moreover the Gaussian P-values can be simply derived from standard F P-values. Furthermore they are exact and valid whatever the data in contrast F P-values are only valid for specially designed simulations. A valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using high dimensional regression and vector autoregression real data sets. The goal is to find valid approximations. The search function is f1st which is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", <doi:10.48550/arXiv.2202.01553>, L. Davies, "An Approximation Based Theory of Linear Regression", 2024, <doi:10.48550/arXiv.2402.09858>.

Create a user-friendly plotting GUI for R'. In addition, one purpose of creating the R package is to facilitate third-party software to call R for drawing, for example, Phoenix WinNonlin software calls R to draw the drug concentration versus time curve.

Conducts 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.

Access Google Cloud machine learning APIs for text and speech tasks. Use the Cloud Translation API for text detection and translation, the Natural Language API to analyze sentiment, entities, and syntax, the Cloud Speech API to transcribe audio to text, and the Cloud Text-to-Speech API to synthesize text into audio files.

Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.

Includes the basic implementation of Genie - a hierarchical clustering algorithm that links two point groups in such a way that an inequity measure (namely, the Gini index) of the cluster sizes does not significantly increase above a given threshold. This method most often outperforms many other data segmentation approaches in terms of clustering quality as tested on a wide range of benchmark datasets. At the same time, Genie retains the high speed of the single linkage approach, therefore it is also suitable for analysing larger data sets. For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>). For an even faster and more feature-rich implementation, including, amongst others, see the genieclust package (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>).

This package implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) <doi:10.1007/s11042-020-10139-6> and M. Clerc (2012) <https://hal.archives-ouvertes.fr/hal-00764996>.

This package provides a sparklyr <https://spark.rstudio.com/> extension that provides an R interface for GraphFrames <https://graphframes.github.io/>. GraphFrames is a package for Apache Spark that provides a DataFrame-based API for working with graphs. Functionality includes motif finding and common graph algorithms, such as PageRank and Breadth-first search.

Scrapes football match shots data from Understat <https://understat.com/> and visualizes it using interactive plots: - A detailed shot map displaying the location, type, and xG value of shots taken by both teams. - An xG timeline chart showing the cumulative xG for each team over time, annotated with the details of scored goals.

This package implements the G-Formula method for causal inference with time-varying treatments and confounders using Bayesian multiple imputation methods, as described by Bartlett et al (2025) <doi:10.1177/09622802251316971>. It creates multiple synthetic imputed datasets under treatment regimes of interest using the mice package. These can then be analysed using rules developed for analysing multiple synthetic datasets.

Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.

This package provides methods to Get Water Attributes Visually in R ('gwavr'). This allows the user to point and click on areas within the United States and get back hydrological data, e.g. flowlines, catchments, basin boundaries, comids, etc.

To create the multiple polygonal point layer for easily discernible shapes, we developed the package, it is like the geom_point of ggplot2'. It can be used to draw the scatter plot.

Given a landscape resistance surface, creates minimum planar graph (Fall et al. (2007) <doi:10.1007/s10021-007-9038-7>) and grains of connectivity (Galpern et al. (2012) <doi:10.1111/j.1365-294X.2012.05677.x>) models that can be used to calculate effective distances for landscape connectivity at multiple scales. Documentation is provided by several vignettes, and a paper (Chubaty, Galpern & Doctolero (2020) <doi:10.1111/2041-210X.13350>).

This package provides tools for interacting with the geographic name resolution service ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated functionality. The GNRS is a batch application for resolving & standardizing political division names against standard name in the geonames database <http://www.geonames.org/>. The GNRS resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the GNRS attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The GNRS works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the GNRS does not attempt to match state or county.

This package provides a ggplot2 extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns.

This is an add-on package to GAMLSS. The purpose of this package is to allow users to fit interval response variables in GAMLSS models. The main function gen.cens() generates a censored version of an existing GAMLSS family distribution.

Annotation of ggplot2 plots with arbitrary TikZ code, using absolute data or relative plot coordinates.

Extend ggplot2 facets to panel layouts arranged in a grid with ragged edges. facet_ragged_rows() groups panels into rows that can vary in length, facet_ragged_cols() does the same but for columns. These can be useful, for example, to represent nested or partially crossed relationships between faceting variables.