This package provides a collection of R Markdown templates for nicely structured, reproducible data analyses in R. The templates have embedded examples on how to write citations, footnotes, equations and use colored message/info boxes, how to cross-reference different parts/sections in the report, provide a nice table of contents (toc) with a References section and proper R session information as well as examples using DT tables and ggplot2 graphs. The bookdown Lite template theme supports code folding.

This package provides functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.

An implementation of calls designed to collect and organize Mastodon data via its Application Program Interfaces (API), which can be found at the following URL: <https://docs.joinmastodon.org/>.

This package provides a test for the well-specification of the linear instrumental variable model. The test is based on trying to predict the residuals of a two-stage least-squares regression using a random forest. Details can be found in Scheidegger, Londschien and Bühlmann (2025) "A residual prediction test for the well-specification of linear instrumental variable models" <doi:10.48550/arXiv.2506.12771>.

It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.

This package provides functions to access data from the Strava v3 API <https://developers.strava.com/>.

The Refugee Population Statistics Database published by The Office of The United Nations High Commissioner for Refugees (UNHCR) contains information about forcibly displaced populations spanning more than 70 years of statistical activities. It covers displaced populations such as refugees, asylum-seekers and internally displaced people, including their demographics. Stateless people are also included, most of who have never been displaced. The database also reflects the different types of solutions for displaced populations such as repatriation or resettlement. More information on the data and methodology can be found on the UNHCR Refugee Data Finder <https://www.unhcr.org/refugee-statistics/>.

Aims at loading Facebook and Instagram advertising data from Smartly.io into R. Smartly.io is an online advertising service that enables advertisers to display commercial ads on social media networks (see <http://www.smartly.io/> for more information). The package offers an interface to query the Smartly.io API and loads data directly into R for further data processing and data analysis.

Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package that implements the RSS and its modified versions for sampling. With RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The RSSampling also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.

Image data used as examples in the loon R package.

This package provides access to and analysis of data from "The Red Book of Endemic Plants of Peru" (León, B., Roque, J., Ulloa, C., Jorgensen, P.M., Pitman, N., Cano, A. 2006) <doi:10.15381/rpb.v13i2.1782>. This package offers comprehensive taxonomic, geographic, and conservation information about Peru's endemic plant species. It includes functions to verify species inclusion, obtain updated taxonomic details, and explore the dataset.

This package provides a Java implementation of the RAKE algorithm ('Rose', S., Engel', D., Cramer', N. and Cowley', W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.

Utility functions to download data from the RESOURCECODE hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See <https://resourcecode.ifremer.fr> for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density.

Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.

This package provides a simple data science challenge system using R Markdown and Dropbox <https://www.dropbox.com/>. It requires no network configuration, does not depend on external platforms like e.g. Kaggle <https://www.kaggle.com/> and can be easily installed on a personal computer.

This package provides functions for reading mass spectrometry data in mzXML format.

Processing and analysis of targeted sequencing data. The package provides a user-friendly interface for core VSEARCH (Rognes et al. (2016), <doi:10.7717/peerj.2584>) functions, in addition to tools for visualization and parameter tuning.

The goal of Rigma is to provide a user friendly client to the Figma API <https://www.figma.com/developers/api>. It uses the latest `httr2` for a stable interface with the REST API. More than 20 methods are provided to interact with Figma files, and teams. Get design data into R by reading published components and styles, converting and downloading images, getting access to the full Figma file as a hierarchical data structure, and much more. Enhance your creativity and streamline the application development by automating the extraction, transformation, and loading of design data to your applications and HTML documents.

Facilities for assessing R packages against a number of metrics to help quantify their robustness.

This package implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang, 2018, Journal of Uncertainty Quantification; Mengyang Gu, Fangzheng Xie and Long Wang, 2022, Journal of Uncertainty Quantification; Mengyang Gu, Kyle Anderson and Erika McPhillips, 2023, Technometrics.

Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...).

Some survey participants tend to respond carelessly which complicates data analysis. This package provides functions that make it easier to explore responses and identify those that may be problematic. See Gottfried et al. (2022) <doi:10.7275/vyxb-gt24> for more information.

Terrestrial laser scanning (TLS) data processing and post-hurricane damage severity classification at the individual tree level using deep Learning. Further details were published in Klauberg et al. (2023) <doi:10.3390/rs15041165>.

Fits linear models with endogenous regressor using latent instrumental variable approaches. The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed. See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>. Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.