This package provides a ggplot2 extension that enables robust image grobs in panels and theme elements.

Selective Sweep can be calculated by five significant Population Genetics Statistics such as "Pi", "Wattersons_theta", "Tajima_D", "Kelly_ZnS" and "Omega" Statistics in specified chromosomal region. It has been developed by using the concept of "Kern" and "Schrider" (2018)<doi:10.1534/g3.118.200262>.

Spline regression, generalized additive models and component-wise gradient boosting utilizing geometrically designed (GeD) splines. GeDS regression is a non-parametric method inspired by geometric principles, for fitting spline regression models with variable knots in one or two independent variables. It efficiently estimates the number of knots and their positions, as well as the spline order, assuming the response variable follows a distribution from the exponential family. GeDS models integrate the broader category of generalized (non-)linear models, offering a flexible approach to model complex relationships. A description of the method can be found in Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023) <doi:10.1016/j.amc.2022.127493>. Further extending its capabilities, GeDS's implementation includes generalized additive models (GAM) and functional gradient boosting (FGB), enabling versatile multivariate predictor modeling, as discussed in the forthcoming work of Dimitrova et al. (2025).

Maps of France in 1830, multivariate datasets from A.-M. Guerry and others, and statistical and graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and development of statistical and graphic methods for multivariate data in a geospatial context of historical interest.

Fit generalized linear mixed models (GLMMs) with normal random effects using first-order Laplace, fully exponential Laplace (FEL) with mean-only corrections, and FEL with mean and covariance corrections in the E-step of an expectation-maximization (EM) algorithm. The current development version provides a matrix-based interface (y, X, Z) and supports binary logit and probit, and Poisson log-link models. An EM framework is used to update fixed effects, random effects, and a single variance component tau^2 for G = tau^2 I, with staged approximations (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A pseudo-likelihood engine glmmFEL_pl() implements the working-response / working-weights linearization approach of Wolfinger and O'Connell (1993) <doi:10.1080/00949659308811554>, and is adapted from the implementation used in the RealVAMS package (Broatch, Green, and Karl (2018)) <doi:10.32614/RJ-2018-033>. The FEL implementation follows Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019> and related work (e.g., Tierney, Kass, and Kadane (1989) <doi:10.1080/01621459.1989.10478824>; Rizopoulos, Verbeke, and Lesaffre (2009) <doi:10.1111/j.1467-9868.2008.00704.x>; Steele (1996) <doi:10.2307/2532845>). Package code was drafted with assistance from generative AI tools.

Promote access to the GESLA <https://gesla787883612.wordpress.com> (Global Extreme Sea Level Analysis) dataset, a higher-frequency sea-level record data from all over the world. It provides functions to download it entirely, or query subsets directly into R, without the need of downloading the full dataset. Also, it provides a built-in web-application, so that users can apply basic filters to select the data of interest, generating informative plots, and showing the selected sites.

This package provides a minimal set of routines to calculate the Grantham distance <doi:10.1126/science.185.4154.862>. The Grantham distance attempts to provide a proxy for the evolutionary distance between two amino acids based on three key chemical properties: composition, polarity and molecular volume. In turn, evolutionary distance is used as a proxy for the impact of missense mutations. The higher the distance, the more deleterious the substitution is expected to be.

Simulation tool to facilitate determination of required sample size to achieve category saturation for studies using multiple repertory grids in conjunction with content analysis.

Add glossaries to markdown and quarto documents by tagging individual words. Definitions can be provided inline or in a separate file.

This package provides a collection of Geoms for R's ggplot2 library. geom_shadowpath(), geom_shadowline(), geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a neon glow around lines to get a steampunk style.

This package provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.

It allows running gretl (<http://gretl.sourceforge.net/index.html>) program from R, R Markdown and Quarto. gretl ('Gnu Regression, Econometrics', and Time-series Library) is a statistical software for Econometric analysis. This package does not only integrate gretl and R but also serves as a gretl Knit-Engine for knitr package. Write all your gretl commands in R', R Markdown chunk.

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

After being given the location of your students submissions and a test file, the function runs each file that is an R script, R Markdown file, or Quarto document, and evaluates the results from all the given tests. Results are neatly returned in a data frame that has a row for each student, and a column for each test.

This package provides a tool for registering (aligning) gene expression profiles between reference and query data.

Computes the sample probability value (p-value) for the estimated coefficient from a standard genome-wide univariate regression. It computes the exact finite-sample p-value under the assumption that the measured phenotype (the dependent variable in the regression) has a known Bernoulli-normal mixture distribution. Finite-sample genome-wide regression p-values (Gwrpv) with a non-normally distributed phenotype (Gregory Connor and Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).

Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.

Use GTFS (General Transit Feed Specification) data for routing from nominated start and end stations, for extracting isochrones', and travel times from any nominated start station to all other stations.

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

These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories.

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.

Scan multiple Git repositories, pull specified files content and process it with large language models. You can summarize the content in specific way, extract information and data, or find answers to your questions about the repositories. The output can be stored in vector database and used for semantic search or as a part of a RAG (Retrieval Augmented Generation) prompt.

Allows user to have graphical user interface to perform analysis of Agricultural experimental data. On using the functions in this package a Interactive User Interface will pop up. Apps Works by simple upload of files in CSV format.

The risk plot may be one of the most commonly used figures in tumor genetic data analysis. We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome.