This package performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized clinical trials. Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>. Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>. Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>. Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <doi:10.1093/biomet/asaf029>. Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025) <doi:10.48550/arXiv.2408.12541>.

This package implements the algorithm by Pourahmadi and Wang (2015) <doi:10.1016/j.spl.2015.06.015> for generating a random p x p correlation matrix. Briefly, the idea is to represent the correlation matrix using Cholesky factorization and p(p-1)/2 hyperspherical coordinates (i.e., angles), sample the angles from a particular distribution and then convert to the standard correlation matrix form. The angles are sampled from a distribution with pdf proportional to sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in Enes Makalic and Daniel F. Schmidt (2018) <arXiv:1809.05212>.

This package offers simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. It works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs, etc). Requires DNA quantitation data or sequenced negative control samples.

Hapassoc performs likelihood inference of trait associations with haplotypes and other covariates in generalized linear models (GLMs). The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs.

This package provides a solution for analyzing digital images of plankton. In combination with ImageJ, an image analysis system, it processes digital images, measures individuals, trains for automatic classification of taxa, and finally, measures plankton samples (abundances, total and partial size spectra or biomasses, etc.).

This package simplifies regression tests by comparing objects produced by test code with earlier versions of those same objects. If objects are unchanged the tests pass, otherwise execution stops with error details. If in interactive mode, tests can be reviewed through the provided interactive environment.

This package helps you create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.

genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer.

Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.

We provide a tidy data structure and visualisations for multiple or grouped variable correlations, general association measures scagnostics and other pairwise scores suitable for numerical, ordinal and nominal variables. Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation.

Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate, life expectancy at birth and net migration (Raftery et al., 2012 <doi:10.1073/pnas.1211452109>). The package can be also used for subnational population projections.

Bayesian analysis of multivariate receptor modeling. The package consists of implementations of the methods of Park and Oh (2015) <doi:10.1016/j.chemolab.2015.08.021>.The package uses JAGS'(Just Another Gibbs Sampler) to generate Markov chain Monte Carlo samples of parameters.

Corbae-Ouliaris frequency domain filtering. According to Corbae and Ouliaris (2006) <doi:10.1017/CBO9781139164863.008>, this is a solution for extracting cycles from time series, like business cycles etc. when filtering. This method is valid for both stationary and non-stationary time series.

This package provides tools for analyzing performances of cricketers based on stats in ESPN Cricinfo Statsguru. The toolset can be used for analysis of Tests,ODIs and Twenty20 matches of both batsmen and bowlers. The package can also be used to analyze team performances.

Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below.

This package provides a shiny gadget to create ggplot2 figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.

Computing economic analysis in civil infrastructure and ecosystem restoration projects is a typical activity. This package contains Standard cost engineering and engineering economics methods that are applied to convert between present, future, and annualized costs. Newnan D. (2020) <ISBN 9780190931919> â Engineering Economic Analysisâ .

This package provides function to transform latex math expressions into format HTML or Office Open XML Math'. The XML result can then be included in HTML', Microsoft Word documents or Microsoft PowerPoint presentations by using a Markdown document or the R package officer'.

This package provides a procedure that fits derivative curves based on a sequence of quotient differences. In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves. In a non-hierarchical setting the package produces a single derivative curve.

Tracks elapsed clock time using a `hms::hms()` scalar. It was was originally developed to time Bayesian model runs. It should not be used to estimate how long extremely fast code takes to execute as the package code adds a small time cost.

Estimates frictional constants for hydraulic analysis of rivers. This HYDRaulic ROughness CALculator (HYDROCAL) was previously developed as a spreadsheet tool and accompanying documentation by McKay and Fischenich (2011, <https://erdc-library.erdc.dren.mil/jspui/bitstream/11681/2034/1/CHETN-VII-11.pdf>).

Allow to visualise data on the NCBI phylogenetic tree as presented in Lifemap <https://lifemap.cnrs.fr/>. It takes as input a dataframe with at least a "taxid" column containing NCBI format TaxIds and allows to draw multiple layers with different visualisation tools.

This package provides functions for fitting monotone polynomials to data. Detailed discussion of the methodologies used can be found in Murray, Mueller and Turlach (2013) <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and Turlach (2016) <doi:10.1080/00949655.2016.1139582>.

The nflverse is a set of packages dedicated to data of the National Football League. This package is designed to make it easy to install and load multiple nflverse packages in a single step. Learn more about the nflverse at <https://nflverse.nflverse.com/>.