River hydrograph separation and daily runoff time series analysis. Provides various filters to separate baseflow and quickflow. Implements advanced separation technique by Rets et al. (2022) <doi:10.1134/S0097807822010146> which involves meteorological data to reveal genetic components of the runoff: ground, rain, thaw and spring (seasonal thaw). High-performance C++17 computation, annually aggregated variables, statistical testing and numerous plotting functions for high-quality visualization.

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>.

This package provides a collection of enhancements to ggplot2', with a focus on creating Universally Designed, accessible graphs easily and quickly.

This package provides a point-and-click shiny interface for the descriptive analysis that comes before any model is chosen. Pass a data frame, pick the variable to describe, and add the layers you want to see it within: a second variable becomes the panels of a ggplot2 facet_wrap(), and further variables become separate figures, one file each, taken either one variable at a time or crossed. Every stratum is reported with the number of observations behind it, on the figure and on each of its panels; strata that contain none are listed rather than dropped, and rows with a missing value in a layer variable are excluded and counted. A continuous variable can be categorized into quantile groups, equal-width bins or user-supplied cut points and then used as a layer; the figure types follow those offered by the ggplotgui package and add the line plot for change over time, an optional LOWESS smoother, and the Kaplan-Meier curve estimated by survival', with an optional number-at-risk table. Columns are described as they are typed, so convert each to the type you mean first. Figures are written as PNG or SVG, and the application prints the ggplot2 code behind the figure on screen, so that a description can be repeated, shared or accounted for later. Everything runs locally, with no network access and no AI involved.

This package provides functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions.

This package provides a framework for creating plots with glowing points.

This package provides a fast C++ implementation of the design-based, Diffusion Decision Model (DDM) and the Linear Ballistic Accumulation (LBA) model. It enables the user to optimise the choice response time model by connecting with the Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampler implemented in the ggdmc package. The package fuses the hierarchical modelling, Bayesian inference, choice response time models and factorial designs, allowing users to build their own design-based models. For more information on the underlying models, see the works by Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>, and Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.

This package implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.

This package provides a simple, opinionated toolkit for visualizing genomic variant data using a ggplot2'-native grammar. Accepts VCF files or plain data frames and produces lollipop plots, consequence summaries, mutational spectrum charts, and cohort-level comparisons as standard ggplot2 objects. Designed for both wet-lab biologists and experienced bioinformaticians.

Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in Martinez-de-Pison (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.

We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees. We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function. The estimators are calculated via an expectation-maximization algorithm.

Identifies gendered words and phrases in text using a built in dictionary of more than two hundred gendered terms paired with gender neutral alternatives. Reports the share of gendered language in a text, lists every gendered term found together with its suggested neutral replacement, and can rewrite a text in gender neutral form. Plain text files are read with base R, while other document formats such as PDF and Word are supported through the optional readtext package. The dictionary is informed by published guidance on gender inclusive language, including the United Nations guidelines <https://www.un.org/en/gender-inclusive-language/> and the European Parliament guidance on gender neutral language.

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

Write SARIMA models in (finite) AR representation and simulate generalized multiplicative seasonal autoregressive moving average (time) series with Normal / Gaussian, Poisson or negative binomial distribution. The methodology of this method is described in Briet OJT, Amerasinghe PH, and Vounatsou P (2013) <doi:10.1371/journal.pone.0065761>.

Allows users to quickly and easily generate fake data containing Personally Identifiable Information (PII) through convenience functions.

Streamline the creation of common charts by taking care of a lot of data preprocessing and plot customization for the user. Provides a high-level interface to create plots using ggplot2'.

This package provides ggplot2 extensions for political map making. Implements new geometries for groups of simple feature geometries. Adds palettes and scales for red to blue color mapping and for discrete maps. Implements tools for easy label generation and placement, automatic map coloring, and themes.

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.

Data sets and scripts used in the book Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.

Numerical integration with Gram polynomials (based on <arXiv:2106.14875> [math.NA] 28 Jun 2021, by Irfan Muhammad [School of Computer Science, University of Birmingham, UK]).

The aim of this package is to offer more variability of graphics based on the self-organizing maps.

This package provides a set of high efficient functions to decode identifiers of National Football League players.

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.

Organize a so-called ragged array as generalized arrays, which is simply an array with sub-dimensions denoting the subdivision of dimensions (grouping of members within dimensions). By the margins (names of dimensions and sub-dimensions) in generalized arrays, operators and utility functions provided in this package automatically match the margins, doing map-reduce style parallel computation along margins. Generalized arrays are also cooperative to R's native functions that work on simple arrays.