Measure single-storage water supply system performance using resilience, reliability, and vulnerability metrics; assess storage-yield-reliability relationships; determine no-fail storage with sequent peak analysis; optimize release decisions for water supply, hydropower, and multi-objective reservoirs using deterministic and stochastic dynamic programming; generate inflow replicates using parametric and non-parametric models; evaluate inflow persistence using the Hurst coefficient.

Branching process simulation model of infectious disease transmission with flexible parameterisation of epidemiology and targeted interventions, including isolation, contact tracing and quarantine, to reduce transmission, together with functions to evaluate outbreak control. Introduced in Hellewell et al. (2020) <doi:10.1016/S2214-109X(20)30074-7>.

Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual "tooltips" for users, among other things.

This package provides methods to easily build requests in the non-standard JSON schema required by the National Institute of Health (NIH)'s RePORTER Project API <https://api.reporter.nih.gov/#/Search/post_v2_projects_search>. Also retrieve and process result sets as either a ragged or flattened tibble'.

The tools and utilities to estimate the model described in "Gremlin's in the Data: Identifying the Information Content of Research Subjects" (Howell et al. (2021) <doi:10.1177/0022243720965930>) using conjoint analysis data such as that collected in Sawtooth Software's Lighthouse or Discover products. Additional utilities are included for formatting the input data.

This package provides tools to help developers and producers manipulate R objects and outputs. It includes tools for displaying results and objects, and for formatting them in the correct format.

The functions in this package compute robust estimators by minimizing a kernel-based distance known as MMD (Maximum Mean Discrepancy) between the sample and a statistical model. Recent works proved that these estimators enjoy a universal consistency property, and are extremely robust to outliers. Various optimization algorithms are implemented: stochastic gradient is available for most models, but the package also allows gradient descent in a few models for which an exact formula is available for the gradient. In terms of distribution fit, a large number of continuous and discrete distributions are available: Gaussian, exponential, uniform, gamma, Poisson, geometric, etc. In terms of regression, the models available are: linear, logistic, gamma, beta and Poisson. Alquier, P. and Gerber, M. (2024) <doi:10.1093/biomet/asad031> Cherief-Abdellatif, B.-E. and Alquier, P. (2022) <doi:10.3150/21-BEJ1338>.

Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.

Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas. It is based on the theoretical results presented in Strothmann et al. (2022) <arXiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.

Streamlines the creation of reproducible analytical pipelines using default.nix expressions generated via the rix package for reproducibility. Define derivations in R', Python or Julia', chain them into a composition of pure functions and build the resulting pipeline using Nix as the underlying end-to-end build tool. Functions to plot the pipeline as a directed acyclic graph are included, as well as functions to load and inspect intermediary results for interactive analysis. User experience heavily inspired by the targets package.

NCL (NCAR Command Language) is one of the most popular spatial data mapping tools in meteorology studies, due to its beautiful output figures with plenty of color palettes designed by experts <https://www.ncl.ucar.edu/index.shtml>. Here we translate all NCL color palettes into R hexadecimal RGB colors and provide color selection function, which will help users make a beautiful figure.

Enables Retrieval-Augmented Generation (RAG) workflows in R by combining local vector search using DuckDB with optional web search via the Tavily API. Supports OpenAI'- and Ollama'-compatible embedding models, full-text and HNSW (Hierarchical Navigable Small World) indexing, and modular large language model (LLM) invocation. Designed for advanced question-answering, chat-based applications, and production-ready AI pipelines. This package is the R equivalent of the python package RAGFlowChain available at <https://pypi.org/project/RAGFlowChain/>.

This package provides a fast implementation of the greedy algorithm for the set cover problem using Rcpp'.

Additional matrix functionality for R including: (1) wrappers for the base matrix function that allow matrices to be created from character strings and lists (the former is especially useful for creating block matrices), (2) better printing of large matrices via the generic "pretty" print function, and (3) a number of convenience functions for users more familiar with other scientific languages like Julia', Matlab'/'Octave', or Python'+'NumPy'.

This is a meta-package designed to support the installation of Rmosek (>= 6.0) and bring the optimization facilities of MOSEK (>= 6.0) to the R-language. The interface supports large-scale optimization of many kinds: Mixed-integer and continuous linear, second-order cone, exponential cone and power cone optimization, as well as continuous semidefinite optimization. Rmosek and the R-language are open-source projects. MOSEK is a proprietary product, but unrestricted trial and academic licenses are available.

Exports an Rcpp interface for the Bessel functions in the Bessel package, which can then be called from the C++ code of other packages. For the original Fortran implementation of these functions see Amos (1995) <doi:10.1145/212066.212078>.

R wrapper for the JPMML-R library <https://github.com/jpmml/jpmml-r>, which converts R models to Predictive Model Markup Language ('PMML').

Density discontinuity testing (a.k.a. manipulation testing) is commonly employed in regression discontinuity designs and other program evaluation settings to detect perfect self-selection (manipulation) around a cutoff where treatment/policy assignment changes. This package implements manipulation testing procedures using local polynomial density estimators: rddensity() constructs test statistics and p-values given a prespecified cutoff, rdbwdensity() performs data-driven bandwidth selection, and rdplotdensity() constructs density plots.

Implementation of the relative placement algorithm widely used in the scoring of Lindy Hop and West Coast Swing dance contests.

Non-inferiority test and diagnostic test are very important in clinical trails. This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test.

Easy installation, loading, and control of packages for redistricting data downloading, spatial data processing, simulation, analysis, and visualization. This package makes it easy to install and load multiple redistverse packages at once. The redistverse is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see <https://alarm-redist.org>.

Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) <doi:10.1016/j.spl.2008.12.001> and Donmez (2010) <https://open.metu.edu.tr/bitstream/handle/11511/19440/index.pdf>.

This package implements the RecMap MP2 construction heuristic <doi:10.1109/INFVIS.2004.57>. This algorithm draws maps according to a given statistical value, e.g., election results, population, or epidemiological data. The basic idea of the RecMap algorithm is that each map region, e.g., different countries, is represented by a rectangle. The area of each rectangle represents the statistical value provided as input to maintain zero cartographic error. Computationally intensive tasks are implemented in C++. The included vignette documents recmap algorithm usage.

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.