This package provides a differential abundance method for the analysis of microbiome data. radEmu estimates fold-differences in the abundance of taxa across samples relative to "typical" fold-differences. Notably, it does not require pseudocounts, nor choosing a denominator taxon. For more details, see Clausen et al. (2026) <doi:10.1093/biomet/asag009>.

This package creates interactive analytic graphs with R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as D3.js graphs embedded in a web page ready for its interactive analysis and exploration.

R access to the FOAAS (F... Off As A Service) web service is provided.

We provide several avenues to predict and account for user-based mortality and tag loss during mark-recapture studies. When planning a study on a target species, the retentionmort_generation() function can be used to produce multiple synthetic mark-recapture datasets to anticipate the error associated with a planned field study to guide method development to reduce error. Similarly, if field data was already collected, the retentionmort() function can be used to predict the error from already generated data to adjust for user-based mortality and tag loss. The test_dataset_retentionmort() function will provide an example dataset of how data should be inputted into the function to run properly. Lastly, the retentionmort_figure() function can be used on any dataset generated from either model function to produce an rmarkdown printout of preliminary analysis associated with the model, including summary statistics and figures. Methods and results pertaining to the formation of this package can be found in McCutcheon et al. (in review, "Predicting tagging-related mortality and tag loss during mark-recapture studies").

Make your phrase or sentence into something funny! Pass a string with the keywords in, and get out a bit of humor.

This package provides clean, tidy access to climate and weather data from the National Oceanic and Atmospheric Administration ('NOAA') via the National Centers for Environmental Information ('NCEI') Data Service API <https://www.ncei.noaa.gov/access/services/data/v1>. Covers daily weather observations, monthly and annual summaries, and 30-year climate normals from over 100,000 stations across 180 countries. No API key is required. Dedicated functions handle the most common datasets, while a generic fetcher provides access to all NCEI datasets. Station discovery functions help users find stations by location or name. Data is downloaded on first use and cached locally for subsequent calls. This package is not endorsed or certified by NOAA'.

The rdmulti package implements estimation, inference, and graphical procedures for regression discontinuity (RD) designs with multiple cutoffs or multiple scores. rdmc() provides point estimation and robust bias-corrected inference for multi-cutoff designs, rdmcplot() provides data-driven RD plots for multi-cutoff designs, and rdms() provides point estimation and robust bias-corrected inference for multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2020_Stata.pdf> for further methodological details.

This package contains the function run.eqs() which calls an EQS script file, executes the EQS estimation, and, finally, imports the results as R objects. These two steps can be performed separately: call.eqs() calls and executes EQS, whereas read.eqs() imports existing EQS outputs as objects into R. It requires EQS 6.2 (build 98 or higher).

This package provides a set of R functions which provide an environment for the Time-Frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals). It was originally written for Splus by Rene Carmona, Bruno Torresani, and Wen L. Hwang, first at the University of California at Irvine and then at Princeton University. Credit should also be given to Andrea Wang whose functions on the dyadic wavelet transform are included. Rwave is based on the book: "Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S", by Rene Carmona, Wen L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press.

Accurately estimates the reliability of cognitive tasks using a fast and flexible permutation-based split-half reliability algorithm that supports stratified splitting while maintaining equal split sizes. See Kahveci, Bathke, and Blechert (2025) <doi:10.3758/s13423-024-02597-y> for details.

This package provides a tidy, vectorized interface to the RxNorm / RxNav API for resolving drug names, RxCUIs, National Drug Codes (NDCs), and related drug concept metadata. The package supports workflows for mapping between drug names, RxCUIs, NDCs, ingredients, products, drug classes, and related concepts using data from the National Library of Medicine's RxNav services <https://lhncbc.nlm.nih.gov/RxNav/APIs/> and RxNorm <https://www.nlm.nih.gov/research/umls/rxnorm/>.

An implementation to compute an optimal dose escalation rule using deep reinforcement learning in phase I oncology trials (Matsuura et al. (2023) <doi:10.1080/10543406.2023.2170402>). The dose escalation rule can directly optimize the percentages of correct selection (PCS) of the maximum tolerated dose (MTD).

R6 class interface for handling relational database connections using DBI package as backend. The class allows handling of connections to e.g. PostgreSQL, MariaDB and SQLite. The purpose is having an intuitive object allowing straightforward handling of SQL databases.

This package provides functions and datasets to support Summary and Analysis of Extension Program Evaluation in R, and An R Companion for the Handbook of Biological Statistics. Vignettes are available at <https://rcompanion.org>.

Sample size and confidence interval calculations in reversible catalytic models, with applications in malaria research. Further details can be found in the paper by Sepúlveda and Drakeley (2015, <doi:10.1186/s12936-015-0661-z>).

This package provides methods for genome-wide association studies (GWAS) using ranked set sampling (RSS) designs. The package includes tools for ranked set sample selection, standard and RSS-based association analyses, simulation of genotype and phenotype data, statistical comparison of RSS and simple random sampling (SRS) approaches, visualization of GWAS results, and power analysis under alternative sampling schemes.

Estimates the total, between-, and within-cluster Spearman rank correlations for continuous and ordinal clustered data. See Tu et al. (2024) <DOI:10.1002/sim.10326> for details.

Analysis of combined total and allele specific reads from the reciprocal cross study with RNA-seq data.

This package provides a method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) <doi:10.1016/j.jspi.2007.11.003>.

An implementation of functionalities to transform directed graphs that are bound to a set of known forbidden paths. There are several transformations, following the rules provided by Villeneuve and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. The resulting graph is generated in a data-frame format. See rsppfp website for more information, documentation an examples.

R interface to the LTP'-Cloud service for Natural Language Processing in Chinese (http://www.ltp-cloud.com/).

Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.

Efficient reading of raw markdown tables into tibbles. Designed to accept content from strings, files, and URLs with the ability to extract and read multiple tables from markdown for analysis.

This package provides a collection of shiny applications for the R package Luminescence'. These mainly, but not exclusively, include applications for plotting chronometric data from e.g. luminescence or radiocarbon dating. It further provides access to bootstraps tooltip and popover functionality and contains the jscolor.js library with a custom shiny output binding.