When you prepare a presentation or a report, you often need to manage a large number of ggplot figures. You need to change the figure size, modify the title, label, themes, etc. It is inconvenient to go back to the original code to make these changes. This package provides a simple way to manage ggplot figures. You can easily add the figure to the database and update them later using CLI (command line interface) or GUI (graphical user interface).

Uses several types of indicator saturation and automated General-to-Specific (GETS) modelling from the gets package and applies it to panel data. This allows the detection of structural breaks in panel data, operationalising a reverse causal approach of causal inference, see Pretis and Schwarz (2022) <doi:10.2139/ssrn.4022745>.

Testing, Implementation and Forecasting of Grey Model (GM(1, 1)). For method details see Hsu, L. and Wang, C. (2007). <doi:10.1016/j.techfore.2006.02.005>.

Modern Parallel Coordinate Plots have been introduced in the 1980s as a way to visualize arbitrarily many numeric variables. This Grammar of Graphics implementation also incorporates categorical variables into the plots in a principled manner. By separating the data managing part from the visual rendering, we give full access to the users while keeping the number of parameters manageably low.

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

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]).

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 provides functions to read in the geometry format under the Neuroimaging Informatics Technology Initiative ('NIfTI'), called GIFTI <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data.

Allows for easy creation of diagnostic plots for a variety of model objects using the Grammar of Graphics. Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.

This package provides methods for fitting macroevolutionary models to phylogenetic trees Pennell (2014) <doi:10.1093/bioinformatics/btu181>.

Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.

This package provides an interactive workflow for visualizing structural equation modeling (SEM), multi-group path diagrams, and network diagrams in R. Users can directly manipulate nodes and edges to create publication-quality figures while maintaining statistical model integrity. Supports integration with lavaan', OpenMx', tidySEM', and blavaan etc. Features include parameter-based aesthetic mapping, generative AI assistance, and complete reproducibility by exporting metadata for script-based workflows.

These Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the Cuba library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).

Generate multiple data sets for educational purposes to demonstrate the importance of multiple regression. The genset function generates a data set from an initial data set to have the same summary statistics (mean, median, and standard deviation) but opposing regression results.

For plant physiologists, converts conductance (e.g. stomatal conductance) to different units: m/s, mol/m^2/s, and umol/m^2/s/Pa.

This package provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) <https://www3.stat.sinica.edu.tw/statistica/J12N1/J12N11/J12N11.html> and further extended by Wu, Tien, and Chen (2010) <doi:10.1016/j.csda.2008.09.029>.

Get distance and travel time between two points from Google Maps. Four possible modes of transportation (bicycling, walking, driving and public transportation).

Companion package for the manual guide-R : Guide pour lâ analyse de données dâ enquêtes avec R available at <https://larmarange.github.io/guide-R/>. guideR implements miscellaneous functions introduced in guide-R to facilitate statistical analysis and manipulation of survey data.

Preview what a ggplot2 plot would look like if you save it to a file. Attach picture dimensions as a canvas() element and get an instant preview. These dimensions will then be used when you save the plot.

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.

It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).

This package provides functions to compute various germination indices such as germinability, median germination time, mean germination time, mean germination rate, speed of germination, Timson's index, germination value, coefficient of uniformity of germination, uncertainty of germination process, synchrony of germination etc. from germination count data. Includes functions for fitting cumulative seed germination curves using four-parameter hill function and computation of associated parameters. See the vignette for more, including full list of citations for the methods implemented.

This package provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

Generator and density function for the Generalized Inverse Gaussian (GIG) distribution.