API client for ClimMob', an open source software for decentralized large-N trials with the tricot approach <https://climmob.net/>. Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions for the target environment, it enables many participants to carry out reasonably simple experiments that taken together can offer even more information. ClimMobTools enables project managers to deep explore and analyse their ClimMob data in R.

This package provides an interactive shiny web application for constructing, analyzing, and visualizing composite indices from multidimensional datasets. Users can upload or select indicator data, group variables into logical categories, apply normalization and weighting methods (such as equal or custom schemes), and compute aggregate composite indices. The shiny interface includes tools for exploring results through tables, plots, and data exports, making it useful for researchers, policymakers, and analysts interested in index-based evaluations.

This package provides a convenient R wrapper to the Comet API, which is a cloud platform allowing you to track, compare, explain and optimize machine learning experiments and models. Experiments can be viewed on the Comet online dashboard at <https://www.comet.com>.

Write executable specifications in a natural language that describes how your code should behave. Write specifications in feature files using Gherkin language and execute them using functions implemented in R. Use them as an extension to your testthat tests to provide a high level description of how your code works.

Simple and seamless access to a variety of StatCan shapefiles for mapping Canadian provinces, regions, forward sortation areas, census divisions, and subdivisions using the popular ggplot2 package.

Collection of utility functions for visualizing body map data collected with the Collaborative Health Outcomes Information Registry.

Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.

Analyzes and modifies metabolomics raw data (generated using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or an Isotopic-Ratio approach. Quality control plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.

Chinese numerals processing in R, such as conversion between Chinese numerals and Arabic numerals as well as detection and extraction of Chinese numerals in character objects and string. This package supports the casual scale naming system and the respective SI prefix systems used in mainland China and Taiwan: "The State Council's Order on the Unified Implementation of Legal Measurement Units in Our Country" The State Council of the People's Republic of China (1984) "Names, Definitions and Symbols of the Legal Units of Measurement and the Decimal Multiples and Submultiples" Ministry of Economic Affairs (2019) <https://gazette.nat.gov.tw/egFront/detail.do?metaid=108965>.

Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.

Statistical tests for the comparison between two correlations based on either independent or dependent groups. Dependent correlations can either be overlapping or nonoverlapping. A web interface is available on the website <http://comparingcorrelations.org>. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from <https://rkward.kde.org> to use this feature. The respective R package rkward cannot be installed directly from a repository, as it is a part of RKWard.

This package provides a flexible interface for interacting with Large Language Model ('LLM') providers including OpenAI', Groq', Anthropic', DeepSeek', DashScope', Gemini', Grok and GitHub Models'. Supports both synchronous and asynchronous chat-completion APIs, with features such as retry logic, dynamic model selection, customizable parameters, and multi-message conversation handling. Designed to streamline integration with state-of-the-art LLM services across multiple platforms.

This package provides a shortcut procedure is proposed to implement closed testing for large-scale multiple testings, especially with the global test. This shortcut is asymptotically equivalent to closed testing and post hoc. Users could detect any possible sets of features or pathways with family-wise error rate controlled. The global test is powerful to detect associations between a group of features and an outcome of interest.

When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) <doi:10.1080/10618600.2022.2071905>.

Evaluation for density and distribution function of convolution of gamma distributions in R. Two related exact methods and one approximate method are implemented with efficient algorithm and C++ code. A quick guide for choosing correct method and usage of this package is given in package vignette. For the detail of methods used in this package, we refer the user to Mathai(1982)<doi:10.1007/BF02481056>, Moschopoulos(1984)<doi:10.1007/BF02481123>, Barnabani(2017)<doi:10.1080/03610918.2014.963612>, Hu et al.(2020)<doi:10.1007/s00180-019-00924-9>.

Designs guide sequences for CRISPR/Cas9 genome editing and provides information on sequence features pertinent to guide efficiency. Sequence features include annotated off-target predictions in a user-selected genome and a predicted efficiency score based on the model described in Doench et al. (2016) <doi:10.1038/nbt.3437>. Users are able to import additional genomes and genome annotation files to use when searching and annotating off-target hits. All guide sequences and off-target data can be generated through the R console with sgRNA_Design() or through crispRdesignR's user interface with crispRdesignRUI(). CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) and the associated protein Cas9 refer to a technique used in genome editing.

Light weight implementation of the standard distribution functions for the chi distribution, wrapping those for the chi-squared distribution in the stats package.

Tables summarizing clinical trial results are often complex and require detailed tailoring prior to submission to a health authority. The crane package supplements the functionality of the gtsummary package for creating these often highly bespoke tables in the pharmaceutical industry.

Check for namespace collisions between a string input (your function or package name) and half a million packages and functions on CRAN.

Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.

This package provides methods and data for color science - color conversions by observer, illuminant, and gamma. Color matching functions and chromaticity diagrams. Color indices, color differences, and spectral data conversion/analysis. This package is deprecated and will someday be removed; for reasons and details please see the README file.

This package implements the instruments for complex-valued modelling, including time series analysis and forecasting. This is based on the monograph by Svetunkov & Svetunkov (2024) <doi: 10.1007/978-3-031-62608-1>.

Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. csa package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.

Offers a set of objects tailored to simplify working with choice data. It enables the computation of choice probabilities and the likelihood of various types of choice models based on given data.