Domain mean estimation with monotonicity or block monotone constraints. See Xu X, Meyer MC and Opsomer JD (2021)<doi:10.1016/j.jspi.2021.02.004> for more details.

This package contains the basic functions to apply the unified framework for partitioning the drivers of stability of ecological communities. Segrestin et al. (2024) <doi:10.1111/geb.13828>.

R interface for RAPIDS cuML (<https://github.com/rapidsai/cuml>), a suite of GPU-accelerated machine learning libraries powered by CUDA (<https://en.wikipedia.org/wiki/CUDA>).

Systematically Run R checks against multiple packages. Checks are run in parallel with strategies to minimize dependency installation. Provides out of the box interface for running reverse dependency check.

Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-à lvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>.

This package provides a domain-specific language for specifying translating recursions into dynamic-programming algorithms. See <https://en.wikipedia.org/wiki/Dynamic_programming> for a description of dynamic programming.

Second and backward-incompatible version of R package eodhd <https://eodhd.com/>, extended with a cache and quota system, also offering functions for cleaning and aggregating the financial data.

The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.

This package provides methods to solve Fuzzy Linear Programming Problems with fuzzy constraints (following different approaches proposed by Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy technological matrix.

Retrieve datasets from the Global Data Lab website <https://globaldatalab.org> directly into R data frames. Functions are provided to reference available options (indicators, levels, countries, regions) as well.

This is a GitHub API wrapper for R. <https://docs.github.com/en/rest> It uses the gh package but has things wrapped up for convenient use cases.

This package provides a tool which allows users the ability to intuitively create flexible, reproducible portable document format reports comprised of aesthetically pleasing tables, images, plots and/or text.

Fast algorithms for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.

This package provides a simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.

Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.

This package provides a novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.

Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of JavaScript in one package.

Data sets for network analysis related to People Analytics. Contains various data sets from the book Handbook of Graphs and Networks in People Analytics by Keith McNulty (2021).

Access a variety of PubMed data through a single, user-friendly interface, including abstracts, bibliometrics from iCite', pubtations from PubTator3', and full-text records from PMC'.

This package provides a novel tool for generating a piecewise constant estimation list of increasingly complex predictors based on an intensive and comprehensive search over the entire covariate space.

This package provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) <doi:10.1007/s00484-016-1161-8>.

Sample size calculations in causal inference with observational data are increasingly desired. This package is a tool to calculate sample size under prespecified power with minimal summary quantities needed.

Multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) (<DOI:10.1111/1755-0998.12665>) for more information.

This package implements the s-values proposed by Ed. Leamer. It provides a context-minimal approach for sensitivity analysis using extreme bounds to assess the sturdiness of regression coefficients.