Command-line and shiny GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.

Reconstruction of muscle fibers from image stacks using textural analysis. Includes functions for tracking, smoothing, cleaning, plotting and exporting muscle fibers. Also calculates basic fiber properties (e.g., length, angle and curvature).

This package provides functions to generate and analyze data for psychology experiments based on the General Recognition Theory.

This package provides diagnostic graphic tools for GLMs, beta-binomial regression model (estimated by VGAM package), beta regression model (estimated by betareg package) and negative binomial regression model (estimated by MASS package). Since most of functions implemented in glmxdiag already exist in other packages, the aim is to provide the user unique functions that work on almost all regression models previously specified. Details about some of the implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993) <doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.

Group Sequential Operating Characteristics for Clinical, Bayesian two-arm Trials with known Sigma and Normal Endpoints, as described in Gerber and Gsponer (2016) <doi: 10.18637/jss.v069.i11>.

Some methods for the inference and clustering of univariate and multivariate functional data, using a generalization of Mahalanobis distance, along with some functions useful for the analysis of functional data. For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.

Solves a least squares system Ax~=b (dim(A)=(m,n) with m >= n) with a precondition matrix B: BAx=Bb (dim(B)=(n,m)). Implemented method is based on GMRES (Saad, Youcef; Schultz, Martin H. (1986). "GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems" <doi:10.1137/0907058>) with callback functions, i.e. no explicit A, B or b are required.

This package performs goodness-of-fit tests for model-based clustering based on the methodology developed in <doi:10.48550/arXiv.2511.04206>. It implements a test statistic derived from empirical likelihood to verify the distribution of clusters in multivariate Gaussian mixtures and other latent class models.

Supports the assessment of the degree of conservation of taxa in conservation systems, both in ex situ (in genebanks, botanical gardens, and other repositories), and in situ (in protected natural areas). Methods are described in Carver et al. (2021) <doi:10.1111/ecog.05430>, building on Khoury et al. (2020) <doi:10.1073/pnas.2007029117>, Khoury et al. (2019) <doi:10.1016/j.ecolind.2018.11.016>, Khoury et al. (2019) <doi:10.1111/DDI.13008>, Castaneda-Alvarez et al. (2016) <doi:10.1038/nplants.2016.22>, and Ramirez-Villegas et al. (2010) <doi:10.1371/journal.pone.0013497>.

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.

Convert GDP time series data from one unit to another. All common GDP units are included, i.e. current and constant local currency units, US$ via market exchange rates and international dollars via purchasing power parities.

This package provides a data visualization design that compares two (usually on a par with each other) data sources on one grid of taichi (yin-yang) diagrams, where the two interlocking fish of every symbol are filled by the two sources, while inheriting ggplot2 features.

This package provides tools for planning and simulating recurrent event trials with overdispersed count endpoints analyzed using negative binomial (or Poisson) rate models. Implements sample size and power calculations for fixed designs with variable accrual, dropout, maximum follow-up, and event gaps, including methods of Zhu and Lakkis (2014) <doi:10.1002/sim.5947> and Friede and Schmidli (2010) <doi:10.3414/ME09-02-0060>. Supports group sequential designs by adding calendar-time analysis schedules compatible with the gsDesign package and by estimating blinded information at interim looks. Includes simulation utilities for recurrent events (including seasonal rates), interim data truncation, and Wald-based inference for treatment rate ratios.

Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127 for more details.

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>.

This package provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.

This package provides a collection of I/O tools for handling the most commonly used genomic datafiles, like fasta/-q, bed, gff, gtf, ped/map and vcf.

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.

Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables. Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>. Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.

This package provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.

This package provides tools for quantitative analysis in gender studies, including functions to calculate various gender inequality metrics such as the Gender Pay Gap, Gender Inequality Index (GII), Gender Development Index (GDI), and Gender Empowerment Measure (GEM). Also includes extracted secondary example datasets for practice and learning purposes, which were obtained from the UNDP Human Development Reports Data Center and the World Bank Gender Data Portal by the author the dataset is available on <doi:10.34740/kaggle/dsv/6359326>. References: Miller, Kevin; Vagins, Deborah J. (2021) <https://eric.ed.gov/?id=ED596219>. Jacques Charmes & Saskia Wieringa (2003) <doi:10.1080/1464988032000125773>. Gaëlle Ferrant (2010) <https://shs.hal.science/halshs-00462463/>.

Moon charts are like pie charts except that the proportions are shown as crescent or gibbous portions of a circle, like the lit and unlit portions of the moon. As such, they work best with only one or two groups. gggibbous extends ggplot2 to allow for plotting multiple moon charts in a single panel and does not require a square coordinate system.

GWAS R API Data Download. This package provides easy access to the NHGRI'-'EBI GWAS Catalog data by accessing the REST API <https://www.ebi.ac.uk/gwas/rest/docs/api/>.

Download and process public domain works in the Project Gutenberg collection <https://www.gutenberg.org/>. Includes metadata for all Project Gutenberg works, so that they can be searched and retrieved.