Facilitates the citation of R packages used in analysis projects. Scans project for packages used, gets their citations, and produces a document with citations in the preferred bibliography format, ready to be pasted into reports or manuscripts. Alternatively, grateful can be used directly within an R Markdown or Quarto document.

Query the Argentine national geographic normalization service georef <https://apis.datos.gob.ar/georef> from R, searching provinces, departments, municipalities, localities, settlements, census units, streets and educational institutions, normalizing addresses and performing reverse geocoding. Results are returned as data frames, and batch queries can be sent in a single request.

Create publication-quality, 2-dimensional visualizations of alpha-helical peptide sequences. Specifically, allows the user to programmatically generate helical wheels and wenxiang diagrams to provide a bird's eye, top-down view of alpha-helical oligopeptides. See Wadhwa RR, et al. (2018) <doi:10.21105/joss.01008> for more information.

This package provides a fast, vectorized hashmap that is built on top of C++ std::unordered_map <https://en.cppreference.com/w/cpp/container/unordered_map.html>. The map can hold any R object as key / value as long as it is serializable and supports vectorized insertion, lookup, and deletion.

Core set of low-level utilities common across the hubverse'. Used to interact with hubverse schema, Hub configuration files and model outputs and designed to be primarily used internally by other hubverse packages. See Reich et al. (2022) <doi:10.2105/AJPH.2022.306831> for an overview of Collaborative Hubs.

Wait for a single key press at the R prompt. This works in terminals, but does not currently work in the Windows GUI', the macOS GUI ('R.app'), in Emacs ESS', in an Emacs shell buffer or in R Studio'. In these cases keypress stops with an error message.

Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the LongCART and SurvCART algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.

This package provides functions, which make matrix creation conciser (such as the core package's function m() for rowwise matrix definition or runifm() for random value matrices). Allows to set multiple matrix values at once, by using list of formulae. Provides additional matrix operators and dedicated plotting function.

This package performs Bayesian meta-analysis, meta-regression and model-based meta-analysis using Stan'. Includes binomial-normal hierarchical models and option to use weakly informative priors for the heterogeneity parameter and the treatment effect parameter which are described in Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.

This package provides a system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).

Given any graph, the node2vec algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <arXiv:1607.00653>.

This package provides a computational toolkit for analyzing nematode communities in ecological studies. Includes methods to quantify nematode-based ecological indicators such as metabolic footprints, energy flow metrics, and community structure. These tools support assessments of soil health, ecosystem functioning, and trophic interactions, standardizing the use of nematodes as bioindicators.

Analyses of OTU tables produced by 16S rRNA gene amplicon sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2017) "Bacterial community composition and dynamics spanning five years in freshwater bog lakes," <doi: 10.1128/mSphere.00169-17>.

Check whether a package is ready for submission to the rOpenSci peer review system ('rOpenSci authors (2026) <doi:10.5281/zenodo.2553043> "'rOpenSci Packages: Development, Maintenance, and Peer Review"). Incorporates the goodpractice package and many additional checks, including aspects related to maintenance of online public code repositories.

This package provides tools to simulate realistic raw case data for an epidemic in the form of line lists and contacts using a branching process. Simulated outbreaks are parameterised with epidemiological parameters and can have age-structured populations, age-stratified hospitalisation and death risk and time-varying case fatality risk.

Companion package that supports the surveydown survey platform (<https://surveydown.org>). The default method for working with a surveydown survey is to edit the plain text survey.qmd and app.R files. With sdstudio', you can create, preview and manage surveys with a shiny application as a graphical user interface.

Estimates correlation coefficients with associated confidence limits for bivariate, partially censored survival times. Uses the iterative multiple imputation approach proposed by Schemper, Kaider, Wakounig and Heinze (2013) <doi:10.1002/sim.5874>. Provides a scatterplot function to visualize the bivariate distribution, either on the original time scale or as copula.

This package provides functions to access the database of 217 data-frames with aggregate study-level characteristics (that may act as effect modifiers) extracted from published systematic reviews with network meta-analysis. The database shall only be used for developing and appraising the methodology to assess the transitivity assumption quantitatively.

Add publication-quality custom legends with vertical brackets. Designed for displaying statistical comparisons between groups, commonly used in scientific publications for showing significance levels. Features include adaptive positioning, automatic bracket spacing for overlapping comparisons, font family inheritance, and support for asterisks, p-values, or custom labels. Compatible with ggplot2 graphics.

This package provides statistical methods for assessing homoscedasticity (equality of variances) across groups. The package implements classical and robust tests for variance homogeneity, together with supporting measures and utilities for exploratory analysis and hypothesis testing. These tools can be used as diagnostic procedures prior to analyses that assume equal variances.

This package implements a grid search algorithm with an adaptive zooming strategy for global optimisation problems with multiple local optima. The method recursively refines the search region around promising grid points, providing reliable initial values for subsequent optimisation procedures. The algorithm is computationally efficient in moderate- to high-dimensional settings.

Fit the reduced-rank multinomial logistic regression model for Markov chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio (2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial logistic regression in Markov chains and reduced-rank. It is very useful in a study where multi-states model is assumed and each transition among the states is controlled by a series of covariates. The key advantage is to reduce the number of parameters to be estimated. The final coefficients for all the covariates and the p-values for the interested covariates will be reported. The p-values for the whole coefficient matrix can be calculated by two bootstrap methods.

Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.

Implementation of JQuery <https://jquery.com> and CSS styles to allow easy incorporation of various social media elements on a page. The elements include addition of share buttons or connect with us buttons or hyperlink buttons to Shiny applications or dashboards and Rmarkdown documents.Sharing capability on social media platforms including Facebook <https://www.facebook.com>, Linkedin <https://www.linkedin.com>, X/Twitter <https://x.com>, Tumblr <https://www.tumblr.com>, Pinterest <https://www.pinterest.com>, Whatsapp <https://www.whatsapp.com>, Reddit <https://www.reddit.com>, Baidu <https://www.baidu.com>, Blogger <https://www.blogger.com>, Weibo <https://www.weibo.com>, Instagram <https://www.instagram.com>, Telegram <https://www.telegram.me>, Youtube <https://www.youtube.com>.