This package provides a procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.

This package provides a ggplot2 extension that supports arbitrary hand-crafted colourable & fillable shapes. New shapes may be feature requested via a Github issue.

The function combines a scatter plot with ridgelines to better visualise the distribution between sample groups. The plot is created with ggplot2'.

This package provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots as described by Archer and Williams (2012) <doi:10.1002/sim.4484>.

At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling activities in order to reduce the overall effort required for generating such plots. For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, called ggPMX to execute them. ggPMX is a toolbox that can generate all diagnostic plots at a quality sufficient for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP <doi:10.1002/psp4.12161>. While not required, you can get/install the R lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, R can use Monolix directly to create the required Chart Data instead of exporting it from the Monolix gui.

Generalized correlation and related measures for assessing nonlinear relationships between variables, including mixed-type data. For methodological background, see <https://r-suzuki.github.io/gcor/method.html>.

This package provides generalized L-moments estimation methods for the generalized extreme value ('GEV') distribution. Implements both stationary GEV and non-stationary GEV11 models where location and scale parameters vary with time. Includes various penalty functions (Martins-Stedinger, Park, Cannon, Coles-Dixon) for shape parameter regularization. Also provides model averaging estimation ('ma.gev') that combines MLE and L-moment methods with multiple weighting schemes for robust high quantile estimation. The GLME methodology is described in Shin et al. (2025a) <doi:10.48550/arXiv.2512.20385>. The non-stationary L-moment method is based on Shin et al. (2025b) <doi:10.1007/s42952-025-00325-3>. The model averaging method is described in Shin et al. (2026) <doi:10.1007/s00477-025-03167-x>. See also Hosking (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x> for L-moments theory and Martins and Stedinger (2000) <doi:10.1029/1999WR900330> for penalized likelihood methods.

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.

Currently provides geom_balance_of_trade(), a ggplot2 layer that fills the area between exports and imports series (with automatic crossing detection and conditional coloring for surplus vs. deficit), and overlays lines and points by default.

This package provides tools.

Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package is for learning purposes and allows users to optimize various functions or parameters by mimicking biological evolution processes such as selection, crossover, and mutation. Ideal for tasks like machine learning parameter tuning, mathematical function optimization, and solving an optimization problem that involves finding the best solution in a discrete space.

This package implements regression models for bounded continuous data in the open interval (0,1) using the five-parameter Generalized Kumaraswamy distribution. Supports modeling all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of predictors through various link functions. Provides efficient maximum likelihood estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics, model comparison tools, and simulation methods. Particularly useful for analyzing proportions, rates, indices, and other bounded response data with complex distributional features not adequately captured by simpler models. Methods are described in Lopes and Bonat (2026) <doi:10.21105/joss.08991>.

Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.

This package provides a collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.

Offers tools for data formatting, anomaly detection, and classification of tree-ring data using spatial comparisons and cross-correlation. Supports flexible detrending and climateâ growth modeling via generalized additive mixed models (Wood 2017, ISBN:978-1498728331) and the mgcv package (<https://CRAN.R-project.org/package=mgcv>), enabling robust analysis of non-linear trends and autocorrelated data. Provides standardized visual reporting, including summaries, diagnostics, and model performance. Compatible with .rwl files and tailored for the Canadian Forest Service Tree-Ring Data (CFS-TRenD) repository (Girardin et al. (2021) <doi:10.1139/er-2020-0099>), offering a comprehensive and adaptable framework for dendrochronologists working with large and complex datasets.

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.

Computes coordinates for the Golosov Relative Size Triangle (RST), classifies party-system observations into Golosov subtypes using polygon mapping and produces publication-ready Relative Size Triangle plots. Method reference: Golosov, G. V. (2011) "Party system classification: A methodological inquiry" <doi:10.1177/1354068810377189>.

This package provides functions for simulating and estimating parameters of various growth models, including Logistic, Exponential, Theta-logistic, Von-Bertalanffy, and Gompertz models. The package supports both simulated and real data analysis, including parameter estimation, visualization, and calculation of global and local estimates. The methods are based on research described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in (<https://www.researchsquare.com/article/rs-2363586/v1>). An interactive web application is also available at [GPEMR Web App](<https://gpem-r.shinyapps.io/GPEM-R/>).

Reproduce the halfnorm() function found in the faraway package using the ggplot2 API.

This package provides methods for processing spatial data for decision-making. This package is an R implementation of methods provided by the open source software GeoFIS <https://www.geofis.org> (Leroux et al. 2018) <doi:10.3390/agriculture8060073>. The main functionalities are the management zone delineation (Pedroso et al. 2010) <doi:10.1016/j.compag.2009.10.007> and data aggregation (Mora-Herrera et al. 2020) <doi:10.1016/j.compag.2020.105624>.

This package provides functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

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.

Functionality for adding the geological timescale to bivariate plots.

This package provides a native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.